Reference List for Machine Learning and its Applications in Geotechnical Engineering

(References are listed in chronological order)

• PART I: NEED TO KNOW KNOWLEDGE

  1 Probability Theory
  [1] Jeffreys, H. (1983). Theory of Probability. Oxford University Press, Oxford, New York.
  [2] Sivia, D.S., and Skilling, J. (2006). Data Analysis: A Bayesian Tutorial. Oxford University Press, New York.
  [3] Ang, A.H.-S., and Tang, W.H. (2007). Probability concepts in engineering, Vol. I Emphasis on applications to civil and environmental engineering, 2nd Ed. Wiley, New York.
  [4] Beck, J.L. (2010). Bayesian system identification based on probability logic. Structural Control and Health Monitoring, 17(7), 825–847.
  [5] Yuen, K.V. (2010). Bayesian Methods for Structural Dynamics and Civil Engineering. John Wiley & Sons, ISBN: 978-0-470-82454-2.
  [6] Gelman, A., et al. (2013). Bayesian Data Analysis, Third Edition. Chapman and Hall/CRC.
  
  2 Information Theory
  [7] Cover, T. M. and J. A. Thomas (1991). Elements of Information Theory. Wiley.
  [8] MacKay, D. J. C. (2003). Information Theory, Inference and Learning Algorithms. Cambridge University Press.
  
  3 Decision Theory
  [9] Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis (Second ed.). Springer.
  [10] Bather, J. (2000). Decision Theory: An Introduction to Dynamic Programming and Sequential Decisions. Wiley.
  
  4 Introductory Materials on Machine Learning
  [11] Mitchell, T. (1997). Machine Learning. McGraw Hill.
  [12] Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer-Verlag New York.
  [13] Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge University Press.
  [14] Farrar, C. R., and Worden, K. (2012). Structural health monitoring: a machine learning perspective. John Wiley and Sons.
  [15] Alpaydin, E. (2014). Introduction to Machine Learning, Third Edition. The MIT Press, London.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  5 Supervised learning
  
  5.1 Decision tree learning
  [16] Breiman, Leo, Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984). Classification and regression trees. Monterey, CA: Wadsworth and Brooks/Cole Advanced Books and Software.
  [17] Dattatreya, G. R., and Kanal, L. N. (1985). Decision trees in pattern recognition. University of Maryland. Computer Science.
  [18] Safavian, S. R., and Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man Cybern., 21(3), 660-674.
  [19] Murthy, S. K., Kasif, S., and Salzberg, S. (1994). A system for induction of oblique decision trees. Journal of Artificial Intelligence Research, 2, 1-32.
  [20] Friedl, M. A., and Brodley, C. E. (1997). Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61(3), 399-409.
  [21] Murthy, S. K. (1998). Automatic construction of decision trees from data: A multi-disciplinary survey. Data Mining and Knowledge Discovery, 2(4), 345-389.
  [22] Kohavi, R., and Quinlan, J. R. (2002). Data mining tasks and methods: Classification: decision-tree discovery. In Handbook of data mining and knowledge discovery (pp. 267-276). Oxford University Press, Inc.
  [23] Rokach, L.; Maimon, O. (2005). Top-down induction of decision trees classifiers-a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C. 35 (4): 476–487.
  [24] Barros, Rodrigo C., Basgalupp, M. P., Carvalho, A. C. P. L. F., Freitas, Alex A. (2012). A Survey of Evolutionary Algorithms for Decision-Tree Induction. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 42(3), 291-312.
  [25] James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning (Vol. 112). New York: springer.
  [26] Lior, R. (2014). Data mining with decision trees: theory and applications (Vol. 81). World scientific.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  5 Supervised learning
  
  5.2 Neural networks
  [27] McCulloch, W. S., and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115-133.
  [28] White, H. (1989). Learning in artificial neural networks: A statistical perspective. Neural Computation, 1(4), 425-464.
  [29] Maren, A., Harston, C., and Pap, R. (1990). Handbook of neural computing applications. Academic Press, Inc., San Diego, California
  [30] Ghaboussi, J., Garrett, J. H., and Wu, X. (1991). Knowledge based modeling of material behaviour with neural networks. Journal of Engineering Mechanics, ASCE, 117(1), 132–153.
  [31] Hunt, K. J., Sbarbaro, D., Żbikowski, R., and Gawthrop, P. J. (1992). Neural networks for control systems—a survey. Automatica, 28(6), 1083-1112.
  [32] Zurada, J. M. (1992). Introduction to artificial neural systems. West Publishing Company, St. Paul.
  [33] Yao, X. (1993). A review of evolutionary artificial neural networks. International Journal of Intelligent Systems, 8(4), 539-567.
  [34] Garrett, J. H. (1994). Where and why artificial neural networks are applicable in civil engineering. Journal of Computing in Civil Engineering, ASCE, 8(2), 129–130.
  [35] Fausett, L. V. (1994). Fundamentals neural networks: Architecture, algorithms, and applications. Prentice-Hall, Inc., Englewood Cliffs, New Jersey.
  [36] Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford University Press.
  [37] Ripley, B. D. (1996). Pattern recognition and neural networks. Cambridge University Press.
  [38] Yao, X., and Liu, Y. (1997). A new evolutionary system for evolving artificial neural networks. IEEE Transactions on Neural Networks, 8(3), 694-713.
  [39] Schalkoff, R. J. (1997). Artificial neural networks (Vol. 1). New York: McGraw-Hill.
  [40] Gardner, M. W., and Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric Environment, 32(14), 2627-2636.
  [41] Zhang, G., Patuwo, B. E., and Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35-62.
  [42] Zhang, G. P. (2000). Neural networks for classification: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 30(4), 451-462.
  [43] Shahin, M. A., Maier, H. R. and Jaksa, M. B. (2005). Investigation into the Robustness of Artificial Neural Network Models for a Case Study in Civil Engineering. In MODSIM 2005 International Congress on Modelling and Simulation, Zerger, A. and Argent, R.M. (eds), Modelling and Simulation Society of Australia and New Zealand, Melbourne, December 12–15, pp. 79–83. ISBN: 0-9758400-2-9.
  [44] Hinton, G. E., and Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  5 Supervised learning
  
  5.3 Deep learning
  [45] Dechter, R. (1986). Learning while searching in constraint-satisfaction problems. University of California, Computer Science Department, Cognitive Systems Laboratory.
  [46] Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., and Ng, A. Y. (2011). Multimodal deep learning. In Proceedings of the 28th International Conference on Machine Learning (ICML-11) (pp. 689-696).
  [47] Deng, L., and Yu, D. (2014). Deep Learning: Methods and Applications. Foundations and Trends in Signal Processing, 7 (3–4), 1–199.
  [48] Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117.
  [49] LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep Learning. Nature, 521, 436-444.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  5 Supervised learning
  
  5.4 Inductive logic programming
  [50] Muggleton, S. H. (1991). Inductive logic programming. New Generation Computing. 8 (4): 295–318.
  [51] Muggleton, S., and De Raedt, L. (1994). Inductive Logic Programming: Theory and methods. The Journal of Logic Programming, 19–20, 629–679.
  [52] Lavrac, N., and Dzeroski, S. (1994). Inductive Logic Programming: Techniques and Applications. New York: Ellis Horwood.
  [53] De Raedt, L. (1999). A perspective on inductive logic programming. In the Logic Programming Paradigm (pp. 335-346). Springer, Berlin, Heidelberg.
  [54] Muggleton, S. (1999). Inductive logic programming: issues, results and the challenge of learning language in logic. Artificial Intelligence, 114(1-2), 283-296.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  5 Supervised learning
  
  5.5 Support vector machines
  [55] Cortes, C., and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
  [56] Cristianini, N., and Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press.
  [57] Ben-Hur, A., Horn, D., Siegelmann, H. T., and Vapnik, V. (2001). Support vector clustering. Journal of Machine Learning Research, 2(Dec), 125-137.
  [58] William H., Teukolsky, Saul A., Vetterling, William T., and Flannery, B. P. (2007). Numerical Recipes: The Art of Scientific Computing (3rd ed.). New York: Cambridge University Press.
  [59] Steinwart, I., and Christmann, A. (2008). Support Vector Machines. Springer-Verlag, New York.
  [60] Chang, C. C., and Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology. 2 (3).
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  5 Supervised learning
  
  5.6 Similarity and metric learning
  [61] Davis, J. V., Kulis, B., Jain, P., Sra, S., and Dhillon, I. S. (2007). Information-theoretic metric learning. International Conference in Machine Learning (ICML): 209–216.
  [62] Kulis, B. (2013). Metric learning: A survey. Foundations and Trends in Machine Learning, 5(4), 287-364.
  [63] Bellet, A., Habrard, A., and Sebban, M. (2013). A survey on metric learning for feature vectors and structured data. arXiv preprint arXiv:1306.6709.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  5 Supervised learning
  
  5.7 Sparse dictionary learning
  [64] Engan, K.; Aase, S. O.; Hakon Husoy, J. (1999). Method of optimal directions for frame design. IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. 5, 443–2446.
  [65] Kreutz-Delgado, K., Murray, J. F., Rao, B. D., Engan, K., Lee, T. W., and Sejnowski, T. J. (2003). Dictionary learning algorithms for sparse representation. Neural Computation, 15(2), 349-396.
  [66] Aharon, M., Elad, M., and Bruckstein, A. (2006). rmK-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(11), 4311-4322.
  [67] Elad, M., and Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image processing, 15(12), 3736-3745.
  [68] Mairal, J., Ponce, J., Sapiro, G., Zisserman, A., and Bach, F. R. (2009). Supervised dictionary learning. In Advances in Neural Information Processing Systems (pp. 1033-1040).
  [69] Mairal, J., Bach, F., Ponce, J., and Sapiro, G. (2009). Online dictionary learning for sparse coding. In Proceedings of the 26th Annual International Conference on Machine Learning (pp. 689-696). ACM.
  [70] Zhang, Q., and Li, B. (2010). Discriminative K-SVD for dictionary learning in face recognition. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on (pp. 2691-2698). IEEE.
  [71] Tosic, I., and Frossard, P. (2011). Dictionary learning. IEEE Signal Processing Magazine, 28(2), 27-38.
  [72] Bengio, Y., Courville, A., and Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.
  [73] Zhang, Z., Xu, Y., Yang, J., Li, X., and Zhang, D. (2015). A survey of sparse representation: algorithms and applications. IEEE Digital Object Identifier access, 3, 490-530.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  6 Semi-supervised learning
  [74] Ratsaby, J., and Venkatesh, S. S. (1995). Learning from a mixture of labeled and unlabeled examples with parametric side information. In Proceedings of The Eighth Annual Conference on Computational Learning Theory (pp. 412-417). ACM.
  [75] Chapelle, O., Scholkopf, B., and Zien, A. (2006). Semi-Supervised Learning, MIT Press, Cambridge, MA.
  [76] Belkin, M., Niyogi, P., and Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7(Nov), 2399-2434.
  [77] Zhu, X. (2008). Semi-supervised learning literature survey. Computer Science, University of Wisconsin-Madison, 2(3), 4.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  7 Reinforcement learning
  [78] Kaelbling, L. P., Littman, M. L., and Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237-285.
  [79] Sutton, R. S., and Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge: MIT press.
  [80] Dorigo, M., Birattari, M., and Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28-39.
  [81] Wiering, M., and Van Otterlo, M. (2012). Reinforcement Learning. Springer.
  [82] Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529.
  [83] Sugiyama, M. (2015). Statistical reinforcement learning: modern machine learning approaches. CRC Press.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  8 Unsupervised learning
  
  8.1 Clustering
  [84] MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability 1(14), 281-297.
  [85] Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of The Royal Statistical Society. Series B (methodological), 1-38.
  [86] Jain, A. K., and Dubes, R. C. (1988). Algorithms for clustering data. Prentice-Hall, Inc.
  [87] Zhang, T., Ramakrishnan, R., and Livny, M. (1996). BIRCH: an efficient data clustering method for very large databases. In ACM Sigmod Record, 25(2), 103-114. ACM.
  [88] Ester, M., Kriegel, H. P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, 96(34), 226-231.
  [89] Ankerst, M., Breunig, M. M., Kriegel, H. P., and Sander, J. (1999). OPTICS: ordering points to identify the clustering structure. In ACM Sigmod record, 28(2), 49-60. ACM.
  [90] Jain, A. K., Murty, M. N., and Flynn, P. J. (1999). Data clustering: a review. ACM Computing Surveys (CSUR), 31(3), 264-323.
  [91] Ng, A. Y., Jordan, M. I., and Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems (pp. 849-856).
  [92] Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., ... and Zhou, Z. H. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1-37.
  [93] McLachlan, G., and Peel, D. (2004). Finite mixture models. John Wiley and Sons.
  [94] Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651-666.
  [95] Han, J., Pei, J., and Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
  [96] Witten, I. H., Frank, E., Hall, M. A., and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  8 Unsupervised learning
  
  8.2 Feature learning (Dimensionality reduction)
  [97] Jolliffe, I. T. (1986). Principal Component Analysis and Factor Analysis. In Principal Component Analysis (pp. 115-128). Springer New York.
  [98] Kohonen, T. (1998). The self-organizing map. Neurocomputing, 21(1), 1-6.
  [99] Tenenbaum, J. B., De Silva, V., and Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319-2323.
  [100] Roweis, S. T., and Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323-2326.
  [101] Hyvärinen, A., Karhunen, J., and Oja, E. (2004). Independent Component Analysis (Vol. 46). John Wiley and Sons.
  [102] He, X., and Niyogi, P. (2004). Locality preserving projections. In Advances in Neural Information Processing Systems,(pp. 153-160).
  [103] Srebro, N., Rennie, J., and Jaakkola, T. S. (2005). Maximum-margin matrix factorization. In Advances in Neural Information Processing Systems, (pp. 1329-1336).
  [104] Hinton, G. E., and Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  8 Unsupervised learning
  
  8.3 Outlier detection
  [105] Knorr, E. M., Ng, R. T., and Tucakov, V. (2000). Distance-based outliers: algorithms and applications. The VLDB Journal—The International Journal on Very Large Data Bases, 8(3-4), 237-253.
  [106] Ramaswamy, S., Rastogi, R., and Shim, K. (2000). Efficient algorithms for mining outliers from large data sets. In ACM Sigmod Record, 29(2), 427-438. ACM.
  [107] Breunig, M. M., Kriegel, H. P., Ng, R. T., and Sander, J. (2000). LOF: identifying density-based local outliers. In ACM sigmod record, 29(2), 93-104). ACM.
  [108] Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., and Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Cmputation, 13(7), 1443-1471.
  [109] He, Z., Xu, X., and Deng, S. (2003). Discovering cluster-based local outliers. Pattern Recognition Letters, 24(9), 1641-1650.
  [110] Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 15.
  [111] Yuen, K.V., and Mu, H.Q. (2012). Novel probabilistic method for robust parametric identification and outlier detection. Probabilistic Engineering Mechanics, 30, 48-59.
  
  8.4 Generative adversarial networks
  [112] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27 (NIPS 2014): 2672-2680.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  9 Bayesian machine learning
  
  9.1 Bayesian network
  [113] Pearl, J. (1985). Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning (UCLA Technical Report CSD-850017). Proceedings of the 7th Conference of the Cognitive Science Society, University of California, Irvine, CA. pp. 329–334.
  [114] Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. San Francisco CA: Morgan Kaufmann.
  [115] Neapolitan, R. E. (1989). Probabilistic reasoning in expert systems: theory and algorithms. Wiley.
  [116] Heckerman, D., Geiger, D., and Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3), 197-243.
  [117] Heckerman, D. (1997). Bayesian networks for data mining. Data Mining and Knowledge Discovery, 1(1), 79-119.
  [118] Friedman, N., Geiger, D., and Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29(2-3), 131-163.
  [119] Friedman, N., Linial, M., Nachman, I., and Pe'er, D. (2000). Using Bayesian networks to analyze expression data. Journal of Computational Biology, 7(3-4), 601-620.
  [120] Neapolitan, R. E. (2004). Learning Bayesian networks. Prentice Hall.
  [121] Nasrabadi, N. M. (2007). Pattern recognition and machine learning. Journal of Electronic Imaging, 16(4), 049901.
  [122] Yuen, K.V., and Ortiz, G.A. (2016). Bayesian nonparametric general regression. International Journal for Uncertainty Quantification, 6(3), 195-213.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  9 Bayesian machine learning
  
  9.2 Bayesian neural network
  [123] Kononenko, I. (1989). Bayesian neural networks. Biological Cybernetics, 61(5), 361-370.
  [124] Wan, E. A. (1990). Neural network classification: A Bayesian interpretation. IEEE Transactions on Neural Networks, 1(4), 303-305.
  [125] MacKay, D. J. (1995). Probable networks and plausible predictions—a review of practical Bayesian methods for supervised neural networks. Network: Computation in Neural Systems, 6(3), 469-505.
  [126] MacKay, D. J. (1995). Bayesian neural networks and density networks. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 354(1), 73-80.
  [127] Thodberg, H. H. (1996). A review of Bayesian neural networks with an application to near infrared spectroscopy. IEEE Transactions on Neural Networks, 7(1), 56-72.
  [128] Barber, D., and Bishop, C. M. (1998). Ensemble Learning in Bayesian Neural Networks. In Neural Networks and Machine Learning, pages 215-237. Springer.
  [129] Lampinen, J., and Vehtari, A. (2001). Bayesian approach for neural networks—review and case studies. Neural Networks, 14(3), 257-274.
  [130] Neal, R. M. (2012). Bayesian learning for neural networks (Vol. 118). Springer Science and Business Media.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  9 Bayesian machine learning
  
  9.3 Gaussian processes
  [131] Barber, D., and Williams, C. K. (1997). Gaussian processes for Bayesian classification via hybrid Monte Carlo. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems NIPS9, pages 340-346, Cambridge, MA, MIT Press.
  [132] MacKay, D. J. (1998). Introduction to Gaussian Processes. In Neural Networks and Machine Learning, volume 168 of NATO advanced study institute on generalization in neural networks and machine learning, pages 133-165. Springer, August.
  [133] C. K. I. Williams and D. Barber. (1998) Bayesian classification with Gaussian processes. IEEE Trans Pattern Analysis and Machine Intelligence, 20, 1342-1351.
  [134] M. Seeger. (2004) Gaussian Processes for Machine Learning. International Journal of Neural Systems, 14(2), 69-106.
  [135] Rasmussen, C. E., and Williams, C. K. (2006). Gaussian processes for machine learning (Vol. 1). Cambridge: MIT press.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  9 Bayesian machine learning
  
  9.4 Relevance vector machine (sparse Bayesian learning)
  [136] Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1(Jun), 211-244.
  [137] Faul, A. C., and Tipping, M. E. (2002). Analysis of sparse Bayesian learning. In Advances in Neural Information Processing Systems (pp. 383-389).
  [138] Tipping, M. E., and Faul, A. C. (2003). Fast marginal likelihood maximization for sparse Bayesian models. In AISTATS, January.
  [139] Wipf, D. P., and Rao, B. D. (2004). Sparse Bayesian learning for basis selection. IEEE Transactions on Signal processing, 52(8), 2153-2164.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  9 Bayesian machine learning
  
  9.5 Bayesian deep learning
  [140] Gal, Y., and Ghahramani, Z. (2016). Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In International Conference on Machine Learning (pp. 1050-1059).
  [141] Wang, H., and Yeung, D. Y. (2016). Towards Bayesian deep learning: A framework and some existing methods. IEEE Transactions on Knowledge and Data Engineering, 28(12), 3395-3408.
  [142] Wang, H., and Yeung, D. Y. (2016). Towards Bayesian deep learning: A survey. arXiv preprint arXiv:1604.01662.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  9 Bayesian machine learning
  
  9.6 Bayesian model class selection and system identification
  [143] Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology, 111-163.
  [144] Jacobs, R. A., Peng, F., and Tanner, M. A. (1997) A Bayesian approach to model selection in hierarchical mixtures of-experts architectures. Neural Networks, 10(2), 231-241.
  [145] Beck, J. L., and Katafygiotis, L. S. (1998). Updating models and their uncertainties. I: Bayesian statistical framework. Journal of Engineering Mechanics, 124(4), 455-461.
  [146] Katafygiotis, L. S., and Beck, J. L. (1998). Updating models and their uncertainties. II: Model identifiability. Journal of Engineering Mechanics, 124(4), 463-467.
  [147] Wasserman, L. (2000). Bayesian model selection and model averaging. Journal of Mathematical Psychology, 44(1), 92-107.
  [148] Corduneanu, A., and Bishop, C. M. (2001). Variational Bayesian Model Selection for Mixture Distributions. In T. Jaakkola and T. Richardson, editors, Artifcial Intelligence and Statistics, pages 27-34. Morgan Kaufmann.
  [149] Beck, J. L., and Yuen, K. V. (2004). Model selection using response measurements: Bayesian probabilistic approach. Journal of Engineering Mechanics, 130(2), 192-203.
  [150] Posada, D., and Buckley, T. R. (2004). Model selection and model averaging in phylogenetics: advantages of Akaike information criterion and Bayesian approaches over likelihood ratio tests. Systematic Biology, 53(5), 793-808.
  [151] Burnham, K. P., and Anderson, D. R. (2004). Multimodel inference: understanding AIC and BIC in model selection. Sociological Methods and Research, 33(2), 261-304.
  [152] Claeskens, G., and Hjort, N. L. (2008). Model selection and model averaging (Vol. 330). Cambridge: Cambridge University Press.
  [153] Yuen, K.-V. (2010). Recent developments of Bayesian model class selection and applications in civil engineering. Structural Safety, 32(5), 338–346.
  

• PART II: MACHINE LEARNING ALGORITHMS
  

  9 Bayesian machine learning
  
  9.7 Simulation-based methods for Bayesian inference (e.g., MCMC, Adaptive MCMC, TMCMC, DREAM, reversible jump MCMC, sequential Monte Carlo, particle filter, BUS)
  [154] Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., and Teller, E. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087-1092.
  [155] Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97-109.
  [156] Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Machine Intell., 6, 721-741.
  [157] Gelman, A., and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 457-472.
  [158] Gilks, W. R. and P. Wild. (1992). Adaptive rejection sampling for Gibbs sampling. Applied Statistics 41(2), 337–348.
  [159] Tierney, L. (1994). Markov chains for exploring posterior distributions. the Annals of Statistics, 1701-1728.
  [160] Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711-732.
  [161] Gelman, A., Roberts, G. O., and Gilks, W. R. (1996). Efficient Metropolis jumping rules. Bayesian Statistics 5, 599–608.
  [162] MacKay, D.J.C. (1998). Introduction to Monte Carlo methods. In M. Jordan, editor, Learning in graphical models. MIT Press.
  [163] Gilks, W. R., Richardson, S., and Spiegelhalter, D. (1998). Markov Chain Monte Carlo in Practice. Boca Raton, FL, Chapman and Hall.
  [164] Doucet, A., Godsill, S., and Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3), 197-208.
  [165] Doucet, A., De Freitas, N., and Gordon, N. (2001). Sequential Monte Carlo Methods in Practice. New York, Springer.
  [166] Doucet, A., Gordon, N. J., and Krishnamurthy, V. (2001). Particle filters for state estimation of jump Markov linear systems. IEEE Transactions on Signal Processing, 49(3), 613-624.
  [167] Beck, J. L. and Au, S. K. (2002). Bayesian updating of structural models and reliability using Markov chain Monte Carlo simulation. Journal of Engineering Mechanics-ASCE, 128(4), 380–391.
  [168] Chopin, N. (2002). A sequential particle filter method for static models. Biometrika, 89(3), 539–552.
  [169] Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174-188.
  [170] Haario, H., Saksman, E., and Tamminen, J. (2005). Componentwise adaptation for high dimensional MCMC. Computational Statistics, 20(2), 265–273.
  [171] Ter Braak, C. J. (2005). Genetic algorithms and Markov Chain Monte Carlo: Differential Evolution Markov Chain makes Bayesian computing easy (revised) (No. 010404). Wageningen UR, Biometris.
  [172] Del Moral, P., Doucet, A., and Jasra, A. (2006). Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(3), 411–436.
  [173] Gamerman, D., and Lopes, H. F. (2006). Markov chain Monte Carlo: stochastic simulation for Bayesian inference. CRC Press.
  [174] Ter Braak, C. J. (2006). A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces. Statistics and Computing, 16(3), 239-249.
  [175] Ching, J. and Chen, Y. (2007). Transitional Markov chain Monte Carlo method for Bayesian model updating, model class selection, and model averaging. Journal of Engineering Mechanics, 133(7), 816–832.
  [176] Vrugt, J. A., Ter Braak, C. J. F., Diks, C. G. H., Robinson, B. A., Hyman, J. M., and Higdon, D. (2009). Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. International Journal of Nonlinear Sciences and Numerical Simulation, 10(3), 273-290.
  [177] Vrugt, J. A., and Ter Braak, C. J. (2011). DREAM (D): an adaptive Markov Chain Monte Carlo simulation algorithm to solve discrete, noncontinuous, and combinatorial posterior parameter estimation problems. Hydrology and Earth System Sciences, 15(12).
  [178] Laloy, E., and Vrugt, J. A. (2012). High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM (ZS) and high‐performance computing. Water Resources Research, 48(1).
  [179] Straub, D., and Papaioannou, I. (2014). Bayesian updating with structural reliability methods. Journal of Engineering Mechanics, 141(3), 04014134.
  [180] Betz, W., Papaioannou, I. and Straub, D. (2014). Adaptive variant of the BUS approach to Bayesian updating. Eurodyn 2014. Porto, Portugal
  [181] DiazDelaO, F. A., Garbuno-Inigo, A., Au, S. K., and Yoshida, I. (2017). Bayesian updating and model class selection with Subset Simulation. Computer Methods in Applied Mechanics and Engineering, 317, 1102-1121.
  [182] Byrnes, P. G., and DiazDelaO, F. A. (2017). Reliability Based Bayesian Inference for Probabilistic Classification: An Overview of Sampling Schemes. In International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 250-263). Springer, Cham.
  [183] Yoshida, I. and Shuku, T. (2020). Bayesian updating of model parameters by iterative particle filter with importance sampling. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 6(2), 04020007
  
  
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  10 Artificial neural networks
  [184] Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2001). Artificial Neural Network Applications in Geotechnical Engineering. Australian Geomechanics, 36(1), 49–62.
  [185] Shahin, M. A., Jaksa, M. B. and Maier, H. R. (2008). Invited Paper: State of the Art of Artificial Neural Networks in Geotechnical Engineering. Electronic Journal of Geotechnical Engineering, 8, 1-26.
  [186] Jaksa, M. B., Maier, H. R. and Shahin, M. A. (2008). Invited Paper: Future Challenges for Artificial Neural Network Modelling in Geotechnical Engineering. Proc. 12th Int. Assoc. for Computer Methods and Advances in Geotech. Engrg. Conference, Goa, India, October 1–6, 1710–1719.
  [187] Shahin, M. A., Jaksa, M. B. and Maier, H. R. (2009). Invited Paper: Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications. Advances in Artificial Neural Systems, Vol. 2009, 308239, 9, DOI: 10.1155/2009/308239.
  [188] Zhang, W., Li, H., Li, Y., Liu, H., Chen, Y., and Ding, X. (2021). Application of deep learning algorithms in geotechnical engineering: a short critical review. Artificial Intelligence Review, https://doi.org/10.1007/s10462-021-09967-1.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  10 Artificial neural networks
  
  10.1 Site Characterization
  [189] Zhou, Y., and Wu, X. (1994). Use of neural networks in the analysis and interpretation of site investigation data. Computer and Geotechnics, 16, 105-122.
  [190] Cal, Y. (1995). Soil classification by neural-network. Advances in Engineering Software, 22(2), 95-97.
  [191] Basheer, I. A., Reddi, L. N., and Najjar, Y. M. (1996). Site characterization by neuronets: An application to the landfill siting problem. Ground Water, 34, 610-617.
  [192] Rizzo, D. M., Lillys, T. P., and Dougherty, D. E. (1996). Comparisons of site characterization methods using mixed data. Geotechnical Special Publication, ASCE, 58(1), 157-179.
  [193] Najjar, Y. M., and Basheer, I. A. (1996). Neural network approach for site characterization and uncertainty prediction. Geotechnical Special Publication, ASCE, 58(1), 134-148.
  [194] Juang C. H., Jiang T., and Christopher R. A. (2001). Three-dimensional site characterization: neural network approach. Geotechnique, 51(9), 799–809.
  [195] Zhang, H., and Yin, S. (2019). Inference of in situ stress from thermoporoelastic borehole breakouts based on artificial neural network. International Journal for Numerical and Analytical Methods in Geomechanics, 43(16), 2493-2511.
  [196] Chen, J., Vissinga, M., Shen, Y., Hu, S., Beal, E., and Newlin, J. (2021). Machine learning–based digital integration of geotechnical and ultrahigh–frequency geophysical data for offshore site characterizations. Journal of Geotechnical and Geoenvironmental Engineering, 147(12), 04021160.
  [197] Kim, Y., Satyanaga, A., Rahardjo, H., Park, H., and Sham, A. W. L. (2021). Estimation of effective cohesion using artificial neural networks based on index soil properties: A Singapore case. Engineering Geology, 289, 106163.
  [198] Sastre, C., Breul, P., Benz Navarette, M., and Bacconnet, C. (2021). Automatic soil identification from penetrometric signal by using artificial intelligence techniques. Canadian Geotechnical Journal, 58(8), 1148-1158.
  [199] Shi, C., and Wang, Y. (2021). Development of subsurface geological cross-section from limited site-specific boreholes and prior geological knowledge using iterative convolution XGBoost. Journal of Geotechnical and Geoenvironmental Engineering, 147(9), 04021082.
  [200] Shi, C. and Wang, Y. (2021). Training image selection for development of subsurface geological cross-section by conditional simulations. Engineering Geology, 295, 106415.
  [201] Shi, C. and Wang, Y. (2021). Smart determination of borehole number and locations for stability analysis of multi-layered slopes using multiple point statistics and information entropy. Canadian Geotechnical Journal, https://doi.org/10.1139/cgj-2020-0327.
  [202] Shi, C. and Wang, Y. (2021). Non-parametric and data-driven interpolation of subsurface soil stratigraphy from limited data using multiple point statistics. Canadian Geotechnical Journal, 58(2), 261 - 280.
  [203] Zhang, J. Z., Phoon, K. K., Zhang, D. M., Huang, H. W., and Tang, C. (2021). Novel approach to estimate vertical scale of fluctuation based on CPT data using convolutional neural networks. Engineering Geology, 294, 106342.
  [204] Depina, I., Jain, S., Mar Valsson, S., and Gotovac, H. (2021). Application of physics-informed neural networks to inverse problems in unsaturated groundwater flow. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1-16.
  [205] Zhang, J. Z., Phoon, K. K., Zhang, D. M., Huang, H. W., and Tang, C. (2021) Novel approach to estimate vertical scale of fluctuation based on CPT data using convolutional neural networks. Engineering Geology. 294, 106342.
  [206] Zhang, J. Z., Zhang, D. M., Huang, H.W., Phoon, K. K., Tang, C., and Li, G. (2021) Hybrid machine learning model with random field and limited CPT data to quantify horizontal scale of fluctuation of soil spatial variability. Acta geotechnica. https://doi.org/10.1007/s11440-021-01360-0
  
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  10 Artificial neural networks
  
  10.2 Geomaterial Properties and Behavior Modeling
  [207] Agrawal, G., Weeraratne, S., and Khilnani, K. (1994). Estimating clay liner and cover permeability using computational neural networks. Proc., First Congress on Computing in Civil Engineering., Washington, June 20-22.
  [208] Gribb, M. M., and Gribb, G. W. (1994). Use of neural networks for hydraulic conductivity determination in unsaturated soil. Proc., 2nd Int. Conf. Ground Water Ecology, J. A. Stanford and H. M. Valett, eds., Bethesda MD: Amer, Water Resources Assoc., 155-163.
  [209] Goh, A. T. C. (1995). Modeling soil correlations using neural networks. Journal of Computing in Civil Engineering, ASCE, 9(4), 275-278.
  [210] Ellis, G. W., Yao, C., Zhao, R., and Penumadu, D. (1995). Stress-strain modelling of sands using artificial neural networks. Journal of Geotechnical Engineering., ASCE, 121(5), 429-435.
  [211] Najjar, Y. M., and Basheer, I. A. (1996). Utilizing computational neural networks for evaluating the permeability of compacted clay liners. Geotechnical and Geological Engineering, 14, 193-221.
  [212] Najjar, Y. M., Basheer, I. A., and McReynolds, R. (1996). Neural modeling of Kansan soil swelling. Transportation Research Record, No. 1526, 14-19.
  [213] Romero, S., and Pamukcu, S. (1996). Characterization of granular material by low strain dynamic excitation and ANN. Geotechnical Special Publication, ASTM-ASCE, 58(2), 1134-1148.
  [214] Penumadu, D., and Jean-Lou, C. (1997). Geomaterial modeling using artificial neural networks. Artificial Neural Networks for Civil Engineers: Fundamentals and Applications, ASCE, 160-184.
  [215] Sidarta, D. E., and Ghaboussi, J. (1998). Constitutive modeling of geomaterials from non-uniform material tests. Computers and Geomechanics, 22(10), 53-71.
  [216] Ghaboussi, J., and Sidarta, D. E. (1998). New nested adaptive neural networks (NANN) for constitutive modeling. Computers and Geotechnics, 22(1), 29-52.
  [217] Zhu, J. H., Zaman, M. M., and Anderson, S. A. (1998). Modeling of soil behavior with a recurrent neural network. Canadian Geotechnical Journal, 35(5), 858-872.
  [218] Zhu, J. H., Zaman, M. M., and Anderson, S. A. (1998). Modelling of shearing behavior of a residual soil with recurrent neural network. International Journal of Numerical and Analytical Methods in Geomechanics, 22(8), 671-687.
  [219] Penumadu, D., and Zhao, R. (1999). Triaxial compression behavior of sand and gravel using artificial neural networks (ANN). Computers and Geotechnics, 24, 207-230.
  [220] Zhang, P., Yin, Z. Y., Jin, Y. F., and Ye, G. L. (2020). An AI-based model for describing cyclic characteristics of granular materials. International Journal for Numerical and Analytical Methods in Geomechanics, 44(9), 1315-1335.
  [221] Zhou, M. L., Shadabfar, M., Huang, H. W., Leung Y. F., and Uchida S. (2020) Meta-modelling of coupled thermo-hydro-mechanical behaviour of hydrate reservoir. Computers and Geotechnics, 128: 103848.
  [222] Heider, Y., Suh, H. S., and Sun, W. (2021). An offline multi-scale unsaturated poromechanics model enabled by self-designed/self-improved neural networks. International Journal for Numerical and Analytical Methods in Geomechanics, 45:1212-1237.
  [223] Liu, D., Liu, H., Wu, Y., Zhang, W., Wang, Y., and Santosh, M. (2021). Characterization of geo-material parameters: Gene concept and big data approach in geotechnical engineering. Geosystems and Geoenvironment, https://doi.org/10.1016/j.geogeo.2021.09.003.
  [224] Xu, J. J., Zhang, H., Tang, C. S., Cheng, Q., Liu, B., and Shi, B. (2021). Automatic soil desiccation crack recognition using deep learning. Geotechnique, 1-13.
  [225] Zhang, P., Jin, Y. F., and Yin, Z. Y. (2021). Machine learning–based uncertainty modelling of mechanical properties of soft clays relating to time-dependent behavior and its application. International Journal for Numerical and Analytical Methods in Geomechanics,45:1588-1602.
  
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  10 Artificial neural networks
  
  10.3 Pile Capacity and Settlement
  [226] Goh, A. T. C. (1994). Nonlinear modelling in geotechnical engineering using neural networks. Australian Civil Engineering Transactions, CE36(4), 293-297.
  [227] Goh, A. T. C. (1995). Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering, 9, 143-151.
  [228] Goh, A. T. C. (1995). Empirical design in geotechnics using neural networks. Géotechnique, 45(4), 709–714.
  [229] Goh, A. T. C. (1996). Pile driving records reanalyzed using neural networks. Journal of Geotechnical Engineering, ASCE, 122(6), 492-495.
  [230] Chan, W. T., Chow, Y. K., and Liu, L. (1995). Neural network: An alternative to pile driving formulas. Computers and Geotechnics, 17, 135-156.
  [231] Lee, I. M., and Lee, J. H. (1996). Prediction of pile bearing capacity using artificial neural networks. Computers and Geotechnics, 18(3), 189-200.
  [232] Abu-Kiefa, M. A. (1998). General regression neural networks for driven piles in cohesionless soils. Journal of Geotechnical and Geoenvironmental Engineering., ASCE, 124(12), 1177-1185
  [233] Teh, C. I., Wong, K. S., Goh, A. T. C., and Jaritngam, S. (1997). Prediction of pile capacity using neural networks. Journal of Computing in Civil Engineering, ASCE, 11(2), 129-138.
  [234] Das, S. K., and Basudhar, P. K. (2006). Undrained lateral load capacity of piles in clay using artificial neural network. Computers and Geotechnics, 33(8), 454–9.
  [235] Shahin, M. A. and Jaksa, M. B. (2009). Intelligent Computing for Predicting Axial Capacity of Drilled Shafts. In Contemporary Topics in In Situ Testing, Analysis, and Reliability of Foundations, Geotechnical Special Publication No. 186, ASCE, pp. 26–33.
  [236] Pooya Nejad, F. and Jaksa, M. B. (2017). Load-settlement Behavior Modeling of Single Piles Using Artificial Neural Networks and CPT Data. Computers and Geotechnics, Vol. 89, pp. 9–21.
  
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  10 Artificial neural networks
  
  10.4 Shallow Foundations
  [237] Sivakugan, N., Eckersley, J. D., and Li, H. (1998). Settlement predictions using neural networks. Australian Civil Engineering Transactions, CE40, 49-52.
  [238] Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2000). Predicting the settlement of shallow foundations on cohesionless soils using back-propagation neural networks. Research Report No. R 167, the University of Adelaide, Adelaide.
  [239] Shahin, M. A., Maier, H. R., and Jaksa, M. B. (2002). Predicting Settlements of Shallow Foundations Using Neural Networks. Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 128, (9), 785–793.
  [240] Shahin, M. A., Jaksa, M. B. and Maier, H. R. (2002). Artificial Neural Network-Based Settlement Prediction Formula for Shallow Foundations on Granular Soils. Australian Geomechanics, 37, (4), 45–52.
  [241] Shahin, M. A., Maier, H. R. and Jaksa, M. B. (2003). Neural and Neurofuzzy Techniques Applied to Modelling Settlement of Shallow Foundations on Granular Soils. International Congress on Modelling and Simulation, MODSIM 2003, D. A. Post (ed.), Townsville, July 14–17, 4, 1886–1891.
  [242] Shahin, M. A., Maier, H. R. and Jaksa, M. B. (2003). Settlement Prediction of Shallow Foundations on Granular Soils Using B-Spline Neurofuzzy Models. Computers and Geotechnics, 30, (8), 637–647.
  [243] Shahin, M. A., Maier, H. R., and Jaksa, M. B. (2004). Data division for developing neural networks applied to geotechnical engineering. Journal of Computing in Civil Engineering, 18(2), 105-114.
  [244] Shahin, M. A., Jaksa, M. B. and Maier, H. R. (2005). Neural Network Based Stochastic Design Charts for Settlement Prediction. Canadian Geotechnical Journal, 42, (1): 110–120.
  [245] Shahin, M. A., Jaksa, M. B. and Maier, H. R. (2005). Stochastic Simulation of Settlement Prediction of Shallow Foundations Based on a Deterministic Artificial Neural Network Model. In MODSIM 2005 International Congress on Modelling and Simulation, Zerger, A. and Argent, R.M. (eds), Modelling and Simulation Society of Australia and New Zealand, Melbourne, December 12–15, pp. 73–78.
  [246] Kuo, Y. L., Jaksa, M. B., Lyamin, A. V. and Kaggwa, W. S. (2009). ANN-based Model for Predicting the Bearing Capacity of Strip Footing on Multi-layered Cohesive Soil. Computers and Geotechnics, 36 (3), 503–516.
  [247] Díaz, E., Brotons, V., and Tomás, R. (2018). Use of artificial neural networks to predict 3-D elastic settlement of foundations on soils with inclined bedrock. Soils and Foundations, 58(6), 1414-1422.
  [248] He, X., Wang, F., Li, W., and Sheng, D. (2021). Efficient reliability analysis considering uncertainty in random field parameters: Trained neural networks as surrogate models. Computers and Geotechnics, 136, 104212.
  [249] Wang, Z. Z., Xiao, C., Goh, S. H., and Deng, M. X. (2021). Metamodel-based reliability analysis in spatially variable soils using convolutional neural networks. Journal of Geotechnical and Geoenvironmental Engineering, 147(3), 04021003.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  10 Artificial neural networks
  
  10.5 Tensile Capacity of Anchors
  [250] Shahin, M. A. and Jaksa, M. B. (2004). Probabilistic Assessment of the Uncertainty Associated with the Pullout Capacity of Marquee Anchors. Proc. 9th Australia New Zealand Conference on Geomechanics, Auckland, February 9–11, Vol. 2, pp. 917–921.
  [251] Shahin, M. A. and Jaksa, M. B. (2005). Neural Network Prediction of Pullout Capacity of Marquee Ground Anchors. Computers and Geotechnics, 32(3), 153–163.
  [252] Shahin, M. A. and Jaksa, M. B. (2005). Modelling the Pullout Capacity of Marquee Ground Anchors Using Neurofuzzy Technique. In MODSIM 2005 International Congress on Modelling and Simulation, Zerger, A. and Argent, R.M. (eds), Modelling and Simulation Society of Australia and New Zealand, Melbourne, December 12–15, pp. 66–72.
  [253] Shahin, M. A. and Jaksa, M. B. (2006). Pullout Capacity of Small Ground Anchors by Direct CPT Methods and Neural Networks. Canadian Geotechnical Journal, 43(6), 626–637.
  [254] Ding, L., Rangaraju, P., and Poursaee, A. (2019). Application of generalized regression neural network method for corrosion modeling of steel embedded in soil. Soils and foundations, 59(2), 474-483.
  [255] Lin, P., Ni, P., Guo, C., and Mei, G. (2020). Mapping soil nail loads using Federal Highway Administration (FHWA) simplified models and artificial neural network technique. Canadian Geotechnical Journal, 57(10), 1453-1471.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  10 Artificial neural networks
  
  10.6 Liquefaction
  [256] Goh, A. T. C. (1994). Seismic liquefaction potential assessed by neural network. Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 120(9), 1467-1480.
  [257] Goh, A. T. C. (1996). Neural-network modeling of CPT seismic liquefaction data. Journal of Geotechnical Engineering, ASCE, 122(1), 70-73.
  [258] Najjar, Y. M., and Ali, H. E. (1998). CPT-based liquefaction potential assessment: A neuronet approach. Geotechnical Special Publication, ASCE, 1, 542-553
  [259] Agrawal, G., Chameau, J. A., and Bourdeau, P. L. (1997). Assessing the liquefaction susceptibility at a site based on information from penetration testing. In Artificial neural networks for civil engineers: fundamentals and applications, N. Kartam, I. Flood, and J. H. Garrett, eds., New York, 185-214.
  [260] Juang, C. H., and Chen, C. J. (1999). CPT-based liquefaction evaluation using artificial neural networks. Computer Aided Civil and Infrastructure Engineering, 14(3), 221-229.
  [261] Goh A. T. C. (2002). Probabilistic neural network for evaluating seismic liquefaction potential. Canadian Geotechnical Journal, 39(2), 219–32.
  [262] Kamura, A., Kurihara, G., Mori, T., Kazama, M., Kwon, Y., Kim, J., and Han, J. T. (2021). Exploring the possibility of assessing the damage degree of liquefaction based only on seismic records by artificial neural networks. Soils and Foundations, 61(3), 658-674.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  10 Artificial neural networks
  
  10.7 Soil Retaining Structures
  [263] Goh, A. T. C., Wong, K. S., and Broms, B. B. (1995). Estimation of lateral wall movements in braced excavation using neural networks. Canadian Geotechnical Journal, 32, 1059-1064.
  [264] Sheil, B. B. (2021). Hybrid framework for forecasting circular excavation collapse: combining physics-based and data-driven modeling. Journal of Geotechnical and Geoenvironmental Engineering, 147(12), 04021140.
  
  10.8 Slope Stability, Landslides, and Debris flows
  [265] Ni, S. H., Lu, P. C., and Juang, C. H. (1996). A fuzzy neural network approach to evaluation of slope failure potential. Microcomputers in Civil Engineering, 11, 59-66.
  [266] Chok, Y. H., Jaksa, M. B., Kaggwa W. S., Griffiths, D. V. and Fenton, G. A. (2016). Neural Network Prediction of the Reliability of Heterogeneous Cohesive Slopes. International Journal for Numerical & Analytical Methods in Geomechanics, 40, 11:1556-1569.
  [267] Qian, Z. G., Li, A. J., Chen, W. C., Lyamin, A. V., and Jiang, J. C. (2019). An artificial neural network approach to inhomogeneous soil slope stability predictions based on limit analysis methods. Soils and foundations, 59(2), 556-569.
  [268] Chen, L., Zhang, W., Gao, X., Wang, L., Li, Z., Böhlke, T., and Perego, U. (2020). Design charts for reliability assessment of rock bedding slopes stability against bi-planar sliding: SRLEM and BPNN approaches. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1–16, https://doi.org/10.1080/17499518.2020.1815215.
  [269] Wang, M. X., Huang, D., Wang, G., and Li, D. Q. (2020). SS-XGBoost: a machine learning framework for predicting newmark sliding displacements of slopes. Journal of Geotechnical and Geoenvironmental Engineering, 146(9), 04020074.
  [270] Cheng, Z., Gong, W., Tang, H., Juang, C.H., Deng, Q., Chen, J., and Ye, X. (2021). UAV photogrammetry-based remote sensing and preliminary assessment of the behavior of a landslide in Guizhou, China. Engineering Geology, 289, 106172.
  [271] Huang, Y., Han, X., and Zhao, L. (2021). Recurrent neural networks for complicated seismic dynamic response prediction of a slope system. Engineering Geology, 289, 106198.
  [272] Lee, D. H., Cheon, E., Lim, H. H., Choi, S. K., Kim, Y. T., and Lee, S. R. (2021). An artificial neural network model to predict debris-flow volumes caused by extreme rainfall in the central region of South Korea. Engineering Geology, 281, 105979.
  [273] Meng, J., Mattsson, H., and Laue, J. (2021). Three-dimensional slope stability predictions using artificial neural networks. International Journal for Numerical and Analytical Methods in Geomechanics, 45:1988-2000.
  [274] Shen, H., Li, J. H., Wang, S. X., and Xie, Z. W. (2021). Prediction of load-displacement performance of grouted anchors in weathered granites using FastICA-MARS as a novel model. Geoscience Frontiers, 12(1), 415-423.
  [275] Tang, L., Ma, Y., Wang, L., Zhang, W., Zheng, L., and Wen, H. (2021). Application of Long Short-Term Memory Neural Network and Prophet Algorithm in Slope Displacement Prediction. ISSMGE International Journal of Geoengineering Case Histories, 6(4), 48-66.
  [276] Wang, Z. Z., and Goh, S. H. (2021). Novel approach to efficient slope reliability analysis in spatially variable soils. Engineering Geology, 281, 105989.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  10 Artificial neural networks
  
  10.9 Tunnels and Underground Openings
  [277] Lee, C., and Sterling, R. (1992). Identifying probable failure modes for underground openings using a neural network. International Journal Rock Mechanics and Mining Science and Geomechanics Abstracts, 29(1), 49-67.
  [278] Sterling, R. L., and Lee, C. A. (1992). A neural network - expert system hybrid approach for tunnel design. Proc., 33rd United-States Symp. Rock Mechanics, J. R. Tillerson and W. R. Wawerisk, eds., Brookfield VT: Balkema, 501-510.
  [279] Moon, H. K., Na, S. M., and Lee, C. W. (1995). Artificial neural-network integrated with expert-system for preliminary design of tunnels and slopes. Proc., 8th Int. Congress on Rock Mechanics, T. Fujii, ed., Rotterdam: Balkema, 1 and 2, 901-905.
  [280] Shi, J. J., Ortigao, J. A. R., and Bai, J. (1998). Modular neural networks for predicting settlement during tunneling. Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 124(5), 389-395.
  [281] Shi, J. J. (2000). Reducing prediction error by transforming I nput data for neural networks. Journal of Computing in Civil Engineering, ASCE, 14(2), 109-116.
  [282] Ferentinou, M., and Fakir, M. (2018). Integrating rock engineering systems device and artificial neural networks to predict stability conditions in an open pit. Engineering Geology, 246, 293-309.
  [283] Huang, H. W., Li, Q. T., and Zhang, D. M., (2018). Deep learning based image recognition for crack and leakage defects of metro shield tunnel. Tunnelling and underground space technology. 77, 166-176.
  [284] Chen, R. P., Zhang, P., Kang, X., Zhong, Z. Q., Liu, Y., and Wu, H. N. (2019). Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods. Soils and Foundations, 59(2), 284-295.
  [285] Chen, J. Y., Zhang, D. M., Huang, H. W., Shadabfar, M., Zhou, M. L., and Yang, T. (2020). Image-based segmentation and quantification of weak interlayers in rock tunnel face via deep learning. Automation in Construction, 120.
  [286] Geng, Z., and Wang, Y. (2020). Physics-guided deep learning for predicting geological drilling risk of wellbore instability using seismic attributes data. Engineering Geology, 279, 105857.
  [287] Huang, H. W., Cheng, W., Zhou, M. L., Chen, J. Y., and Zhao, S, (2020). Towards automated 3D inspection of water leakages in shield tunnel linings using mobile laser scanning data. Sensors, 20(22): 6669.
  [288] Huang, H. W., Zhao, S., Zhang, D. M., and Chen, J. Y. (2020) Deep learning-based instance segmentation of cracks from shield tunnel lining images. Structure and Infrastructure Engineering. 1-14. https://doi.org/10.1080/15732479.2020.1838559
  [289] Zhao, S., Zhang, D. M., and Huang, H. W. (2020) Deep learning–based image instance segmentation for moisture marks of shield tunnel lining. Tunnelling and Underground Space Technology. 95, 1-11, 103156. https://doi.org/10.1016/j.tust.2019.103156
  [290] Chen, J. Y., Yang, T., Zhang, D. M., Huang, H. W., and Tian, Y. (2021). Deep learning based classification of rock structure of tunnel face. Geoscience Frontiers, 12(1), 395–404.
  [291] Chen, J. Y., Zhou, M. L., Huang, H. W., Zhang, D. M., and Peng, Z. (2021). Automated extraction and evaluation of fracture trace maps from rock tunnel face images via deep learning. International Journal of Rock Mechanics and Mining Sciences, 142(March).
  [292] Chen, J. Y., Zhou, M. L., Zhang, D. M., Huang, H. W., & Zhang, F. (2021). Quantification of water inflow in rock tunnel faces via convolutional neural network approach. Automation in Construction, 123 (December 2020).
  [293] Guo, D., Li, J., Jiang, S. H., Li, X., and Chen, Z. Y. (2021). Intelligent assistant driving method for tunnel boring machine based on big data. Acta Geotechnica, https://doi.org/10.1007/s11440-021-01327-1.
  [294] Kovačević, M. S., Bačić, M., and Gavin, K. (2021). Application of neural networks for the reliability design of a tunnel in karst rock mass. Canadian Geotechnical Journal, 58(4), 455-467.
  [295] Li, J. H., Li, P. X., Guo, D., Li, X., and Chen, Z. Y. (2021). Advanced prediction of tunnel boring machine performance based on big data. Geoscience Frontiers, 12(1), 331-338.
  [296] Xue Y. D, Jia F, Cai X. Y., Shadabfar M., and Huang H. W. (2021). An optimization strategy to improve the deep learning-based recognition model of leakage in shield tunnels. Computer-Aided Civil and Infrastructure Engineering, 1-17. https://doi.org/10.1111/mice.12731
  [297] Xue Y. D., Shi P. Z., Jia F., and Huang H. W. (2021). 3D reconstruction and automatic leakage defect quantification of metro tunnel based on SfM-Deep learning method. Underground Space, https://doi.org/10.1016/j.undsp.2021.08.004
  [298] Zhou M. L., Shadabfar M., Xue Y. D., and Zhang Y. (2021) Probabilistic analysis of tunnel roof deflection under sequential excavation using ANN-Based Monte Carlo Simulation and Simplified Reliability Approach. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7(4): 04021043.
  [299] Zhang, J. Z., Phoon, K. K., Zhang, D. M., Huang, H. W., and Tang, C. (2021) Deep learning-based evaluation of factor of safety with confidence interval for tunnel deformation in spatially variable soil. Journal of Rock Mechanics and Geotechnical Engineering, https://doi.org/10.1016/j.jrmge.2021.09.001
  [300] Zhao, S., Shadabfar, M., Zhang, D. M, Chen, J. Y, and Huang, H. W., (2021) Deep learning-based classification and instance segmentation of leakage-area and scaling images of shield tunnel linings. Structural Control and Health Monitoring. 1-22, e2732. https://doi.org/10.1002/stc.2732
  [301] Zhao, S., Zhang, D. M., Xue, Y., Zhou, M. and Huang, H. (2021). A deep learning-based approach for refined crack evaluation from shield tunnel lining images, Automation in Construction, 132. https://doi.org/10.1016/j.autcon.2021.103934
  [302] Zhou M. L., Cheng W., Huang H. W., and Chen J. Y. (2021). A novel approach to automated 3D spalling defects inspection in railway tunnel linings using laser intensity and depth information. Sensors, 21(17): 5725.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  10 Artificial neural networks
  
  10.10 Ground Improvement
  [303] Ranasinghe, R. A. T. M., Jaksa, M. B., Kuo, Y. L. and Pooya Nejad, F. (2017). Application of Artificial Neural Networks for Predicting the Impact of Rolling Dynamic Compaction Using Dynamic Cone Penetrometer Test Results. Journal of Rock Mechanics and Geotechnical Engineering, 9(2), 340–349.
  [304] Ranasinghe, R. A. T. M., Jaksa, M. B., Pooya Nejad, F. and Kuo, Y. L. (2017). Predicting the Effectiveness of Rolling Dynamic Compaction Using Genetic Programming and Cone Penetration Test Data. Proc. of Institution of Civil Engineers – Ground Improvement, 170(4), 193–207.
  [305] Hosseini, S. A. A., Mojtahedi, S. F. F., and Sadeghi, H. (2020). Optimisation of deep mixing technique by artificial neural network based on laboratory and field experiments. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 14(2), 142-157.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  11 Support vector machine
  
  11.1 Site Characterization
  [306] Odeh, I. O. A., Chittleborough, D. J., and McBratney, A. B. (1992). Soil pattern recognition with fuzzy-c-means: application to classification and soil-landform interrelationships. Soil Science Society of America Journal, 56(2), 505-516.
  [307] Bhattacharya, B., and Solomatine, D. P. (2006). Machine learning in soil classification. Neural Networks, 19(2), 186-195.
  [308] Sitharam, T. G., Samui, P., and Anbazhagan, P. (2008). Spatial variability of rock depth in Bangalore using geostatistical, neural network and support vector machine models. Geotechnical and Geological Engineering, 26(5), 503-517.
  [309] Yu, L., Porwal, A., Holden, E. J., and Dentith, M. C. (2012). Towards automatic lithological classification from remote sensing data using support vector machines. Computers and Geosciences, 45, 229-239.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  11 Support vector machine
  
  11.2 Geomaterial Properties and Behavior Modeling
  [310] Feng, X. T., Zhao, H., and Li, S. (2004). A new displacement back analysis to identify mechanical geo‐material parameters based on hybrid intelligent methodology. International Journal for Numerical and Analytical Methods in Geomechanics, 28(11), 1141-1165.
  [311] Zhao, H. B., and Yin, S. (2009). Geomechanical parameters identification by particle swarm optimization and support vector machine. Applied Mathematical Modelling, 33(10), 3997-4012.
  [312] Tinoco, J., Correia, A. G., and Cortez, P. (2011). A data mining approach for predicting jet grouting geomechanical parameters. In Road Materials and New Innovations in Pavement Engineering (pp. 97-104).
  [313] Ceryan, N., Okkan, U., Samui, P., and Ceryan, S. (2013). Modeling of tensile strength of rocks materials based on support vector machines approaches. International Journal for Numerical and Analytical Methods in Geomechanics, 37(16), 2655-2670.
  [314] Ceryan, N. (2014). Application of support vector machines and Bayesian neural network machines in predicting uniaxial compressive strength of volcanic rocks. Journal of African Earth Sciences, 100, 634-644.
  
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  11 Support vector machine
  
  11.3 Pile Capacity
  [315] Samui, P. (2008). Prediction of friction capacity of driven piles in clay using the support vector machine. Canadian Geotechnical Journal, 45(2), 288-295.
  [316] Pal, M., and Deswal, S. (2008). Modeling pile capacity using support vector machines and generalized regression neural network. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 134(7), 1021-1024.
  [317] Samui, P. (2011). Prediction of pile bearing capacity using support vector machine. International Journal of Geotechnical Engineering, 5(1), 95-102.
  [318] Tinoco, J., Correia, A. G., and Cortez, P. (2014). Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns. Computers and Geotechnics, 55, 132-140.
  [319] Kordjazi, A., Nejad, F. P., and Jaksa, M. B. (2014). Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data. Computers and Geotechnics, 55, 91-102.
  [320] Kordjazi, A., Pooya Nejad, F. and Jaksa, M. B. (2014). Prediction of Ultimate Bearing Capacity of Axially Loaded Piles Using a Support Vector Machine Based on CPT Data. Computers and Geotechnics, 55, (1), 91–102.
  [321] Kordjazi, A., Pooya Nejad, F. and Jaksa, M. B. (2015). Prediction of Load-carrying Capacity of Piles Using a Support Vector Machine and Improved Data Collection. Proc. 12th Australia New Zealand Conference on Geomechanics, ANZ 2015, Wellington, February 22–25, 8 pp.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  11 Support vector machine
  
  11.4 Settlement of Foundations
  [322] Samui, P., and Sitharam, T. G. (2008). Least‐square support vector machine applied to settlement of shallow foundations on cohesionless soils. International Journal for Numerical and Analytical Methods in Geomechanics, 32(17), 2033-2043.
  [323] Samui, P. (2008). Support vector machine applied to settlement of shallow foundations on cohesionless soils. Computers and Geotechnics, 35(3), 419-427.
  
  11.5 Liquefaction
  [324] Pal, M. (2006). Support vector machines‐based modelling of seismic liquefaction potential. International Journal for Numerical and Analytical Methods in Geomechanics, 30(10), 983-996.
  [325] Pal, M. (2006). Support vector machines‐based modelling of seismic liquefaction potential. International Journal for Numerical and Analytical Methods in Geomechanics, 30(10), 983-996.
  [326] Goh, A. T., and Goh, S. H. (2007). Support vector machines: their use in geotechnical engineering as illustrated using seismic liquefaction data. Computers and Geotechnics, 34(5), 410-421.
  [327] Samui, P., and Karthikeyan, J. (2013). Determination of liquefaction susceptibility of soil: a least square support vector machine approach. International Journal for Numerical and Analytical Methods in Geomechanics, 37(9), 1154-1161.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  11 Support vector machine
  
  11.6 Soil Retaining Walls and Dams
  [328] Zheng, D., Cheng, L., Bao, T., and Lv, B. (2013). Integrated parameter inversion analysis method of a CFRD based on multi-output support vector machines and the clonal selection algorithm. Computers and Geotechnics, 47, 68-77.
  [329] Ji, Z., Wang, B., Deng, S., and You, Z. (2014). Predicting dynamic deformation of retaining structure by LSSVR-based time series method. Neurocomputing, 137, 165-172.
  [330] Ranković, V., Grujović, N., Divac, D., and Milivojević, N. (2014). Development of support vector regression identification model for prediction of dam structural behaviour. Structural Safety, 48, 33-39.
  [331] Fisher, W. D., Camp, T. K., and Krzhizhanovskaya, V. V. (2016). Crack detection in earth dam and levee passive seismic data using support vector machines. Procedia Computer Science, 80, 577-586.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  11 Support vector machine
  
  11.7 Slope Stability
  [332] Feng, X. T., Zhao, H., and Li, S. (2004). Modeling non-linear displacement time series of geo-materials using evolutionary support vector machines. International Journal of Rock Mechanics and Mining Sciences, 41(7), 1087-1107.
  [333] Samui, P. (2008). Slope stability analysis: a support vector machine approach. Environmental Geology, 56(2), 255.
  [334] Zhao, H. B. (2008). Slope reliability analysis using a support vector machine. Computers and Geotechnics, 35(3), 459-467.
  [335] Tan, X. H., Bi, W. H., Hou, X. L., and Wang, W. (2011). Reliability analysis using radial basis function networks and support vector machines. Computers and Geotechnics, 38(2), 178-186.
  [336] Li, S., Zhao, H. B., and Ru, Z. (2013). Slope reliability analysis by updated support vector machine and Monte Carlo simulation. Natural Hazards, 65(1), 707-722.
  [337] Samui, P., Lansivaara, T., and Bhatt, M. R. (2013). Least square support vector machine applied to slope reliability analysis. Geotechnical and Geological Engineering, 31(4), 1329-1334.
  [338] Li, B., Li, D., Zhang, Z., Yang, S., and Wang, F. (2015). Slope stability analysis based on quantum-behaved particle swarm optimization and least squares support vector machine. Applied Mathematical Modelling, 39(17), 5253-5264.
  [339] Kang, F., and Li, J. (2015). Artificial bee colony algorithm optimized support vector regression for system reliability analysis of slopes. Journal of Computing in Civil Engineering, 30(3), 04015040.
  [340] Kang, F., Li, J. S., and Li, J. J. (2016). System reliability analysis of slopes using least squares support vector machines with particle swarm optimization. Neurocomputing, 209, 46-56.
  [341] Kang, F., Xu, Q., and Li, J. J. (2016). Slope reliability analysis using surrogate models via new support vector machines with swarm intelligence. Applied Mathematical Modelling, 40(11), 6105-6120.
  [342] Li, S., Zhao, H., Ru, Z., and Sun, Q. (2016). Probabilistic back analysis based on Bayesian and multi-output support vector machine for a high cut rock slope. Engineering Geology, 203, 178-190.
  [343] Zeng, P., Zhang, T., Li, T., Jimenez, R., Zhang, J., and Sun, X. (2020). Binary classification method for efficient and accurate system reliability analyses of layered soil slopes. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1-17.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  11 Support vector machine
  
  11.8 Tunnels and Underground Openings
  [344] Yao, B. Z., Yang, C. Y., Yao, J. B., and Sun, J. (2010). Tunnel surrounding rock displacement prediction using support vector machine. International Journal of Computational Intelligence Systems, 3(6), 843-852.
  [345] Jiang, A. N., Wang, S. Y., and Tang, S. L. (2011). Feedback analysis of tunnel construction using a hybrid arithmetic based on support vector machine and particle swarm optimization. Automation in Construction, 20(4), 482-489.
  [346] Zhou, J., Li, X., and Shi, X. (2012). Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Safety Science, 50(4), 629-644.
  [347] Mahdevari, S., Haghighat, H. S., and Torabi, S. R. (2013). A dynamically approach based on SVM algorithm for prediction of tunnel convergence during excavation. Tunnelling and Underground Space Technology, 38, 59-68.
  [348] Mahdevari, S., Shahriar, K., Yagiz, S., and Shirazi, M. A. (2014). A support vector regression model for predicting tunnel boring machine penetration rates. International Journal of Rock Mechanics and Mining Sciences, 72, 214-229.
  [349] Li, X., Li, X., and Su, Y. (2016). A hybrid approach combining uniform design and support vector machine to probabilistic tunnel stability assessment. Structural Safety, 61, 22-42.
  [350] Zhao, H. (2021). A reduced order model based on machine learning for numerical analysis: An application to geomechanics. Engineering Applications of Artificial Intelligence, 100, 104194.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  11 Support vector machine
  
  11.9 Landslide
  [351] Feng, X. T., Hudson, J. A., Li, S., Zhao, H., Gao, W., and Zhang, Y. (2004). Integrated intelligent methodology for large-scale landslide prevention design. International Journal of Rock Mechanics and Mining Sciences, 41, 750-755.
  [352] Yao, X., Tham, L. G., and Dai, F. C. (2008). Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology, 101(4), 572-582.
  [353] Kavzoglu, T., and Colkesen, I. (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11(5), 352-359.
  [354] Marjanovic, M., Bajat, B., and Kovacevic, M. (2009). Landslide susceptibility assessment with machine learning algorithms. In Intelligent Networking and Collaborative Systems, 2009. INCOS'09. International Conference on (pp. 273-278). IEEE.
  [355] Marjanović, M., Kovačević, M., Bajat, B., and Voženílek, V. (2011). Landslide susceptibility assessment using SVM machine learning algorithm. Engineering Geology, 123(3), 225-234.
  [356] Xu, C., Dai, F., Xu, X., and Lee, Y. H. (2012). GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology, 145, 70-80.
  [357] Bui, D. T., Pradhan, B., Lofman, O., and Revhaug, I. (2012). Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and Naive Bayes Models. Mathematical Problems in Engineering, 2012.
  [358] Ballabio, C., and Sterlacchini, S. (2012). Support vector machines for landslide susceptibility mapping: the Staffora River Basin case study, Italy. Mathematical Geosciences, 44(1), 47-70.
  [359] Pourghasemi, H. R., Jirandeh, A. G., Pradhan, B., Xu, C., and Gokceoglu, C. (2013). Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. Journal of Earth System Science, 122(2), 349-369.
  [360] Li, X. Z., and Kong, J. M. (2014). Application of GA–SVM method with parameter optimization for landslide development prediction. Natural Hazards and Earth System Sciences, 14(3), 525-533.
  [361] Bui, D. T., Tuan, T. A., Klempe, H., Pradhan, B., and Revhaug, I. (2016). Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13(2), 361-378.
  [362] Hong, H., Pradhan, B., Jebur, M. N., Bui, D. T., Xu, C., and Akgun, A. (2016). Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines. Environmental Earth Sciences, 75(1), 40.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  11 Support vector machine
  
  11.10 Railway track
  [363] Li S. T., Xue, Y. D., Shen, K. Wang X. F., and Luo H. (2021). Mortar layer void detection of ballastless track using the impact echo method based on support vector machine[J]. IOP Conference Series: Earth and Environmental Science. IOP Publishing, 861(7): 072022. https://doi.org/10.1088/1755-1315/861/7/072022
  [364] Luo W., Shen, K., Wang X. F., and Xue, Y. D. (2021). Experimental study on void defects detection of ballastless track mortar layer based on FFT and WT[J]. IOP Conference Series: Earth and Environmental Science. IOP Publishing, 861(2): 022043. https://doi.org/10.1088/1755-1315/861/2/022043
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  12 Clustering
  
  12.1 Site Characterization
  [365] Judd, A. G. (1980). The use of cluster analysis in the derivation of geotechnical classifications. Bulletin of the Association of Engineering Geologists, 17(4), 193-211.
  [366] Hegazy, Y. A. (1998). Delineating Geostratigraphy by Cluster Analysis of Piezocone Data. Ph. D Thesis, Georgia Institute of Technology, 464 pp.
  [367] Hegazy, Y. A., and Mayne, P. W. (2002). Objective site characterization using clustering of piezocone data. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 128(12), 986-996.
  [368] Facciorusso, J., and Uzielli, M. (2004). Stratigraphic profiling by cluster analysis and fuzzy soil classification from mechanical cone penetration tests. In Geotechnical and Geophysical Site Characterization (Proceedings ISC-2, Portugal), pp. 905-912, (Millpress: Rotterdam).
  [369] Facciorusso, J., and Uzielli, M. (2004). Stratigraphic profiling by cluster analysis and fuzzy soil classification from mechanical cone penetration tests. Proc. of ISC-2 on Geotechnical and Geophysical Site Characterization, Porto, Millpress, Rotterdam, 905-912.
  [370] Das, S. K., and Basudhar, P. K. (2009). Utilization of self-organizing map and fuzzy clustering for site characterization using piezocone data. Computers and Geotechnics, 36(1-2), 241-248.
  [371] Walvoort, D. J. J., Brus, D. J., and De Gruijter, J. J. (2010). An R package for spatial coverage sampling and random sampling from compact geographical strata by k-means. Computers and Geosciences, 36(10), 1261-1267.
  [372] Ferentinou, M. D., Hasiotis, T., and Sakellariou, M. G. (2010). Clustering of Geotechnical Properties of Marine Sediments Through Self—Organizing Maps: An Example from the Zakynthos Canyon—Valley System, Greece. In Submarine Mass Movements and Their Consequences (pp. 43-54). Springer, Dordrecht.
  [373] Bashari, A., Beiki, M., and Talebinejad, A. (2011). Estimation of deformation modulus of rock masses by using fuzzy clustering-based modeling. International Journal of Rock Mechanics and Mining Sciences, 48(8), 1224-1234.
  [374] Ferentinou, M., Hasiotis, T., and Sakellariou, M. (2012). Application of computational intelligence tools for the analysis of marine geotechnical properties in the head of Zakynthos canyon, Greece. Computers and Geosciences, 40, 166-174.
  [375] Riquelme, A. J., Abellán, A., Tomás, R., and Jaboyedoff, M. (2014). A new approach for semi-automatic rock mass joints recognition from 3D point clouds. Computers and Geosciences, 68, 38-52.
  [376] Masoud, A. A. (2016). Geotechnical site suitability mapping for urban land management in Tanta District, Egypt. Arabian Journal of Geosciences, 9(5), 340.
  [377] Wang, X., Wang, H., Liang, R. Y., and Liu, Y. (2019). A semi-supervised clustering-based approach for stratification identification using borehole and cone penetration test data. Engineering Geology, 248, 102-116.
  [378] Godoy, C., Depina, I., and Thakur, V. (2020). Application of machine learning to the identification of quick and highly sensitive clays from CPTu tests. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 21.6 (2020): 445-461.
  [379] Molina-Gómez, F., da Fonseca, A. V., Ferreira, C., Sousa, F., and Bulla-Cruz, L. A. (2021). Defining the soil stratigraphy from seismic piezocone data: A clustering approach. Engineering Geology, 287, 106111.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  12 Clustering
  
  12.2 Liquefaction
  [380] Garcia, S. R., Romo, M. P., and Botero, E. (2008). A neurofuzzy system to analyze liquefaction-induced lateral spread. Soil Dynamics and Earthquake Engineering, 28(3), 169-180.
  
  12.3 Slope Stability
  [315] [381] Tang, X. S., Li, D. Q., Chen, Y. F., Zhou, C. B., and Zhang, L. M. (2012). Improved knowledge-based clustered partitioning approach and its application to slope reliability analysis. Computers and Geotechnics, 45, 34-43.
  [382] Wang, Y., Huang, J., and Tang, H. (2020). Automatic identification of the critical slip surface of slopes. Engineering Geology, 273, 105672.
  
  12.4 Lifeline Engineering
  [383] Toprak, S., Nacaroglu, E., Cetin, O. A., and Koc, A. C. (2009). Pipeline damage assessment using cluster analysis. In TCLEE 2009: Lifeline Earthquake Engineering in a Multihazard Environment (pp. 1-8).
  [384] Jayaram, N., and Baker, J. W. (2010). Efficient sampling and data reduction techniques for probabilistic seismic lifeline risk assessment. Earthquake Engineering and Structural Dynamics, 39(10), 1109-1131.
  [385] Sun, J., Wang, R., Wang, X., Yang, H., and Ping, J. (2014). Spatial cluster analysis of bursting pipes in water supply networks. Procedia Engineering, 70, 1610-1618.
  [386] Lim, H. W., Song, J., and Kurtz, N. (2015). Seismic reliability assessment of lifeline networks using clustering‐based multi‐scale approach. Earthquake Engineering and Structural Dynamics, 44(3), 355-369.
  
  12.5 Landslide
  [387] Gorsevski, P. V., Gessler, P. E., and Jankowski, P. (2003). Integrating a fuzzy k-means classification and a Bayesian approach for spatial prediction of landslide hazard. Journal of Geographical Systems, 5(3), 223-251.
  [388] Alimohammadlou, Y., Najafi, A., and Gokceoglu, C. (2014). Estimation of rainfall-induced landslides using ANN and fuzzy clustering methods: a case study in Saeen Slope, Azerbaijan province, Iran. Catena, 120, 149-162.
  [389] Melchiorre, C., Matteucci, M., Azzoni, A., and Zanchi, A. (2008). Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology, 94(3-4), 379-400.
  [390] Ding, M., and Hu, K. (2014). Susceptibility mapping of landslides in Beichuan County using cluster and MLC methods. Natural Hazards, 70(1), 755-766.
  
  12.6 Tunnels and Underground Openings
  [391] Chen, J., Huang, H., Zhou, M., and Chaiyasarn, K. (2021). Towards semi-automatic discontinuity characterization in rock tunnel faces using 3D point clouds. Engineering Geology, 291(May), 106232.
  [392] Zhou, M. L., Chen, J., Huang, H. W., Zhang, D. M., & Zhao, S. (2021). Multi-source data driven method for assessing the rock mass quality of a NATM tunnel face via hybrid ensemble learning models. International Journal of Rock Mechanics and Mining Sciences, 147(September), 104914.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  13 Feature learning (Dimensionality reduction)
  
  13.1 Soil Retaining Walls
  [393] Hashash, Y. M., Levasseur, S., Osouli, A., Finno, R., and Malecot, Y. (2010). Comparison of two inverse analysis techniques for learning deep excavation response. Computers and Geotechnics, 37(3), 323-333.
  
  13.2 Slope Stability
  [394] He, H., Li, S., Sun, H., and Yang, T. (2011). Environmental factors of road slope stability in mountain area using principal component analysis and hierarchy cluster. Environmental Earth Sciences, 62(1), 55-59.
  [395] Crosta, G. B., Frattini, P., and Agliardi, F. (2013). Deep seated gravitational slope deformations in the European Alps. Tectonophysics, 605, 13-33.
  
  13.3 Tunnels and Underground Openings
  [396] Yun, H. B., Park, S. H., Mehdawi, N., Mokhtari, S., Chopra, M., Reddi, L. N., and Park, K. T. (2014). Monitoring for close proximity tunneling effects on an existing tunnel using principal component analysis technique with limited sensor data. Tunnelling and Underground Space Technology, 43, 398-412.
  
  13.4 Landslide
  [397] Micheletti, N., Foresti, L., Robert, S., Leuenberger, M., Pedrazzini, A., Jaboyedoff, M., and Kanevski, M. (2014). Machine learning feature selection methods for landslide susceptibility mapping. Mathematical Geosciences, 46(1), 33-57.
  
  13.5 Offshore Engineering
  [398] Spaulding, M. L., Grilli, A., Damon, C., and Fugate, G. (2010). Application of technology development index and principal component analysis and cluster methods to ocean renewable energy facility siting. Marine Technology Society Journal, 44(1), 8-23.
  
  13.6 Others
  [399] Blatman, G., and Sudret, B. (2008). Sparse polynomial chaos expansions and adaptive stochastic finite elements using a regression approach. Comptes Rendus Mécanique, 336(6), 518-523.
  [400] Blatman, G., and Sudret, B. (2011). Adaptive sparse polynomial chaos expansion based on least angle regression. Journal of Computational Physics, 230(6), 2345-2367.
  [401] Blatman, G., and Sudret, B. (2010). An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis. Probabilistic Engineering Mechanics, 25(2), 183-197.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  14 Outlier detection
  
  14.1 Site Characterization
  [402] Halim, I. S., and Tang, W. H. (1993). Site exploration strategy for geologic anomaly characterization. Journal of geotechnical engineering, 119(2), 195-213.
  [403] Kim, H. S., Chung, C. K., and Kim, H. K. (2016). Geo-spatial data integration for subsurface stratification of dam site with outlier analyses. Environmental Earth Sciences, 75(2), 168.
  [404] Zheng, S., Zhu, Y. X., Li, D. Q., Cao, Z. J., Deng, Q. X., Phoon, K. K. (2021). Probabilistic outlier detection for sparse multivariate geotechnical site investigation data using Bayesian learning. Geoscience Frontiers, 12(1), 425-439.
  
  14.2 Others
  [405] Yuen, K.V., and Ortiz, G.A. (2017). Outlier detection and robust regression for correlated data. Computer Methods in Applied Mechanics and Engineering, 313, 632-646.
  [406] Bozorgzadeh, N., and Bathurst, R. J. (2019). Bayesian model checking, comparison and selection with emphasis on outlier detection for geotechnical reliability-based design. Computers and Geotechnics, 116, 103181.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  15 Bayesian machine learning
  
  15.1 Site Characterization
  [407] Tang, W. H. (1971). A Bayesian evaluation of information for foundation engineering design. In Proc., 1st International Conf. on Applications of Statistics and Probability to Soil and Structural Engineering, Hong Kong.
  [408] Tang, W. H. (1987). Updating anomaly statistics - single anomaly case. Structural Safety, 4(2), 151-163.
  [409] Nobre, M. M. and Sykes, J. F. (1992). Application of Bayesian Kriging to subsurface characterization. Canadian Geotechnical Journal, 29(4): 589-598.
  [410] Honjo, Y., and Kudo, N. (1998). Matching objective and subjective information in geotechnical inverse analysis based on entropy minimization. Proc. International Symp. on Inverse Problems in Engineering Mechanics, Nagano, Japan, 263--271.
  [411] McGrath, T. and Gilbert, R. (1999). Analytical method for designing and analyzing 1D search programs. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 125(12), 1043-1056.
  [412] Ditlevsen, O., Tarp-Johansen, N. J. and Denver, H. (2000). Bayesian soil assessments combining prior with posterior censored samples. Computers and Geotechnics, 26(3-4), 187-198.
  [413] Zhang, L. M., Tang, W. H., Zhang, L. L., and Zheng, J. G., (2004). Reducing uncertainty of prediction from empirical correlations. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 130(5), 526-534.
  [414] Jung, B. C., Gardoni, P., Biscontin, A. (2008). Probabilistic soil identification based on cone penetration tests. Geotechnique, 58(7), 591-603.
  [415] Cetin, K. and Ozan, C. (2009). CPT-Based Probabilistic Soil Characterization and Classification. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 135(1), 84-107.
  [416] Ching, J., Phoon, K. K., and Chen, Y. C., (2010). Reducing shear strength uncertainties in clays by multivariate correlations. Canadian Geotechnical Journal, 47 (1), 16–33.
  [417] Wang, Y., Au, S. K. and Cao, Z. J. (2010). Bayesian approach for probabilistic characterization of sand friction angles. Engineering Geology, 114 (3–4), 354–363.
  [418] Ching, J., Chen, J. R., Yeh, J. Y., and Phoon, K. K., (2012). Updating uncertainties in friction angles of clean sands. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 138 (2), 217–229.
  [419] Houlsby, N. M. T. and Houlsby, G. T. (2013). Statistical fitting of undrained strength data. Geotechnique, 63(14), 1253-1263.
  [420] Uzielli, M. and Mayne, P. W. (2013). Bayesian characterization of transformation uncertainty for strength and stiffness of sands. Foundation Engineering in the Face of Uncertainty: Honoring Fred H. Kulhawy (GSP 229), ASCE, Reston, VA, 368-384.
  [421] Wang, Y. and Cao, Z. J. (2013) Probabilistic characterization of Young’s modulus of soil using equivalent samples. Engineering Geology, 159, 106–118.
  [422] Cao, Z. J. and Wang, Y. (2013) Bayesian approach for probabilistic site characterization using cone penetration tests. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 139 (2), 267–276.
  [423] Wang, Y., Huang, K. and Cao, Z. J. (2013) Probabilistic identification of underground soil stratification using cone penetration tests. Canadian Geotechnical Journal, 50 (7), 766–776.
  [424] Cao, Z. J. and Wang, Y. (2014) Bayesian model comparison and selection of spatial correlation functions for soil parameters. Structural Safety, 49, 10–17.
  [425] Cao, Z. J., Huang, K., and Wang, Y. (2014). Bayesian inverse analysis for geotechnical site characterization using cone penetration test. International Journal of Reliability and Safety, 8(2-4), 97-116.
  [426] Cao, Z. J. and Wang, Y. (2014) Bayesian model comparison and characterization of undrained shear strength. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 140 (6), 04014018, 1–9.
  [427] Feng, X. and Jimenez, R. (2014). Bayesian prediction of elastic modulus of intact rocks using their uniaxial compressive strength. Engineering Geology, 173(1), 32-40.
  [428] Müller, R., Larsson, S. and Spross, J. (2014). Extended multivariate approach for uncertainty reduction in the assessment of undrained shear strength in clays. Canadian Geotechnical Journal, 51(3), 231-245.
  [429] Wang, Y., Huang, K. and Cao, Z. J. (2014) Bayesian identification of soil strata in London Clay. Geotechnique, 64 (3), 239–246.
  [430] Wang, Y., Zhao, T. Y. and Cao, Z. J. (2015) Site-specific probability distribution of geotechnical properties. Computers and Geotechnics, 70, 159–168.
  [431] Wang, Y. and Aladejare, A. E. (2015). Selection of site-specific regression model for characterization of uniaxial compressive strength of rock. International Journal of Rock Mechanics and Mining Sciences, 75, 73-81.
  [432] Ching, J., Wu, S. S., and Phoon, K. K. (2015). Statistical characterization of random field parameters using frequentist and Bayesian approaches. Canadian Geotechnical Journal, 53(2), 285-298.
  [433] Cao, Z. J., Wang, Y. and Li, D. Q. (2016). Quantification of prior knowledge in geotechnical site characterization. Engineering Geology, 203, 107–116.
  [434] Cao, Z. J., Wang, Y., and Li, D. Q. (2016). Site-specific characterization of soil properties using multiple measurements from different test procedures at different locations–A Bayesian sequential updating approach. Engineering Geology, 211, 150-161.
  [435] Ching, J., and Wang, J. S. (2016). Application of the transitional Markov chain Monte Carlo to probabilistic site characterization. Engineering Geology, 203, 151–167.
  [436] Wang, Y. and Aladejare, A. E. (2016). Bayesian characterization of correlation between uniaxial compressive strength and Young’s modulus of rock. International Journal of Rock Mechanics and Mining Sciences, 85, 10–19.
  [437] Wang, Y. and Akeju, O. V. (2016). Quantifying the cross-correlation between effective cohesion and friction angle of soil from limited site-specific data. Soils and Foundations, 56(6), 1057–1072.
  [438] Wang, Y., Akeju, O. V., and Cao, Z. J. (2016). Bayesian Equivalent Sample Toolkit (BEST): an Excel VBA program for probabilistic characterisation of geotechnical properties from limited observation data. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 10(4), 251-268.
  [439] Wang, Y., Cao, Z. J. and Li, D. Q. (2016). Bayesian perspective on geotechnical variability and site characterization. Engineering Geology, 203, 117–125.
  [440] Wang, Y. and Zhao, T. (2016). Interpretation of soil property profile from limited measurement data: a compressive sampling perspective. Canadian Geotechnical Journal, 53(9), 1547-1559.
  [441] Wang, Y. and Aladejare, A. E. (2016). Evaluating variability and uncertainty of Geological Strength Index at a specific site. Rock Mechanics and Rock Engineering, 49(9), 3559–3573.
  [442] Tian, M., Li, D. Q., Cao, Z. J., Phoon, K. K., and Wang, Y. (2016). Bayesian identification of random field model using indirect test data. Engineering Geology, 210, 197-211.
  [443] Wang, Y. and Aladejare, A. E. (2016). Evaluating variability and uncertainty of Geological Strength Index at a specific site. Rock Mechanics and Rock Engineering, 49(9), 3559–3573.
  [444] Akeju, O. V., Senetakis, K., and Wang, Y. (2017). Bayesian parameter identification and model selection for normalized modulus reduction curves of soils. Journal of Earthquake Engineering, 1-29.
  [445] Aladejare, A. E., and Wang, Y. (2017). Sources of uncertainty in site characterization and their impact on geotechnical reliability-based design. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 3(4), 04017024.
  [446] Ching, J. and Phoon, K. K. (2017). Characterizing uncertain site-specific trend function by sparse Bayesian learning, ASCE Journal of Engineering Mechanics, 143(7), 04017028.
  [447] Wang, Y., Akeju, O. V., and Zhao, T. (2017). Interpolation of spatially varying but sparsely measured geo-data: A comparative study. Engineering Geology, 231, 200-217.
  [448] Wang, Y., Arroyo, M., Cao, Z. J., Ching, J., Länsivaara, T., Orr, T., and Simpson, B. (2017). Selection of characteristic values for rock and soil properties using Bayesian statistics and prior knowledge. Joint TC205/TC304 Working Group on Discussion of statistical/reliability methods for Eurocodes, ISSMGE.
  [449] Wang, Y. and Zhao, T. (2017). Statistical interpretation of soil property profiles from sparse data using Bayesian Compressive Sampling. Geotechnique, 67(6), 523-536.
  [450] Wang, Y. and Zhao, T. (2017). Bayesian assessment of site-specific performance of geotechnical design charts with unknown model uncertainty. International Journal for Numerical and Analytical Methods in Geomechanics, 41(5), 781-800.
  [451] Aladejare, A. E. and Wang, Y. (2017). Evaluation of rock property variability. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 11(1), 22-41.
  [452] Huang, J., Zheng, D., Li, D., Kelly, R., and Sloan, S. W. (2017). Probabilistic characterization of 2D soil profile by integrating CPT with MASW data. Canadian Geotechnical Journal, 10.1139/cgj-2017-0429.
  [453] Zhao, T., Montoya-Noguera, S., Phoon, K. K., and Wang, Y. (2018). Interpolating spatially varying soil property values from sparse data for facilitating characteristic value selection. Canadian Geotechnical Journal, 55(2), 171-181.
  [454] Ching, J., Phoon, K. K., Beck, J. L., and Huang, Y., (2018). Identifiability of Geotechnical Site-Specific Trend Functions, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 2017, 3 (4): 04017021.
  [455] Ching, J., Wu, T., Stuedlein, A., and Bong, T. (2018) Estimating horizontal scale of fluctuation with limited CPT soundings. Geoscience Frontiers, 10.1016/j.gsf.2017.11.008.
  [456] Shen, M. Y., Cao, Z. J., Li, D. Q., and Wang, Y. (2018). Probabilistic characterization of site-specific inherent variability of undrained shear strength using both indirect and direct measurements. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 4(1), 04017038.
  [457] Wang, L., Cao, Z. J., Li, D. Q., Phoon, K. K., and Au, S. K. (2018). Determination of site-specific soil-water characteristic curve from a limited number of test data-A Bayesian perspective, Geoscience Frontiers, 10.1016/j.gsf.2017.10.014.
  [458] Zhang, L., Li, D. Q., Tang, X. S., Cao, Z. J., and Phoon, K. K. (2018). Bayesian model comparison and characterization of bivariate distribution for shear strength parameters of soil. Computers and Geotechnics, 95, 110-118.
  [459] Wang, Y., Zhao, T., and Phoon, K. K. (2018). Direct simulation of random field samples from sparsely measured geotechnical data with consideration of uncertainty in interpretation. Canadian Geotechnical Journal, https://doi.org/10.1139/cgj-2017-0254.
  [460] Huang, J., Zheng, D., Li, D. Q., Kelly, R., and Sloan, S. W. (2018). Probabilistic characterization of two-dimensional soil profile by integrating cone penetration test (CPT) with multi-channel analysis of surface wave (MASW) data. Canadian Geotechnical Journal, 55(8), 1168-1181.
  [461] Krogstad, A., Depina, I., and Omre, H. (2018). Cone penetration data classification by Bayesian inversion with a Hidden Markov model. In Journal of Physics: Conference Series (Vol. 1104, No. 1, p. 012015). IOP Publishing.
  [462] Wang, Y., Zhao, T., and Phoon, K. K. (2018). Direct simulation of random field samples from sparsely measured geotechnical data with consideration of uncertainty in interpretation. Canadian Geotechnical Journal, 55(6), 862-880.
  [463] Yoshida, I., Tasaki, Y., Otake, Y. and Wu, S. (2018). Optimal sampling placement in a Gaussian random field based on value of information. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 4(3). https://doi.org/10.1061/AJRUA6.0000970.
  [464] Zhang, L., Li, D. Q., Tang, X. S., Cao, Z. J., and Phoon, K. K. (2018). Bayesian model comparison and characterization of bivariate distribution for shear strength parameters of soil. Computers and Geotechnics, 95, 110-118.
  [465] Zhao, T., Hu, Y., and Wang, Y. (2018). Statistical interpretation of spatially varying 2D geo-data from sparse measurements using Bayesian compressive sampling. Engineering Geology, 246, 162-175.
  [466] Zhao, T. and Wang, Y. (2018). Simulation of cross-correlated random field samples from sparse measurements using Bayesian compressive sensing. Mechanical Systems and Signal Processing, 112, 384-400.
  [467] Montoya-Noguera, S., Zhao, T., Hu, Y., Wang, Y., and Phoon, K. K. (2019). Simulation of non-stationary non-Gaussian random fields from sparse measurements using Bayesian compressive sampling and Karhunen-Loève expansion. Structural Safety, 79, 66-79.
  [468] Bozorgzadeh, N., Harrison, J. P., and Escobar, M. D. (2019). Hierarchical Bayesian modelling of geotechnical data: application to rock strength. Géotechnique, 69(12), 1056-1070.
  [469] Cao, Z. J., Zheng, S., Li, D. Q., and Phoon, K. K. (2019). Bayesian identification of soil stratigraphy based on soil behaviour type index. Canadian Geotechnical Journal, 56(4), 570-586.
  [470] Ching, J., and Phoon, K. K. (2019). Constructing site-specific multivariate probability distribution model using Bayesian machine learning. Journal of Engineering Mechanics, 145(1), 04018126.
  [471] Hu, Y., Zhao, T., Wang, Y., Choi, C., and Ng, C. W. W. (2019). Direct simulation of two-dimensional isotropic or anisotropic random field from sparse measurement using Bayesian compressive sampling. Stochastic Environmental Research and Risk Assessment, 33(8-9), 1477-1496.
  [472] Namikawa, T. (2019). Evaluation of statistical uncertainty of cement-treated soil strength using Bayesian approach. Soils and Foundations, 59(5), 1228-1240.
  [473] Wang, H., Wang, X., Wellmann, J. F., and Liang, R. Y. (2019). A Bayesian unsupervised learning approach for identifying soil stratification using cone penetration data. Canadian Geotechnical Journal, 56(8), 1184-1205.
  [474] Wang, Y., Guan, Z., and Zhao, T. (2019). Sample size determination in geotechnical site investigation considering spatial variation and correlation. Canadian Geotechnical Journal, 56(7), 992-1002.
  [475] Wang, Y., Zhao, T., and Phoon, K. K. (2019). Statistical inference of random field auto-correlation structure from multiple sets of incomplete and sparse measurements using Bayesian compressive sampling-based bootstrapping. Mechanical Systems and Signal Processing, 124, 217-236.
  [476] Wang, Y., Zhao, T., Hu, Y., and Phoon, K. K. (2019). Simulation of random fields with trend from sparse measurements without detrending. Journal of Engineering Mechanics, ASCE, 145(2), 04018130.
  [477] Zhao, T., and Wang, Y. (2019). Determination of efficient sampling locations in geotechnical site characterization using information entropy and Bayesian compressive sampling. Canadian Geotechnical Journal, 56(11), 1622-1637.
  [478] Bozorgzadeh, N., and Bathurst, R. J. (2020). Hierarchical Bayesian approaches to statistical modelling of geotechnical data. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1-18.
  [479] Ching, J., and Phoon, K. K. (2020). Constructing a site-specific multivariate probability distribution using sparse, incomplete, and spatially variable (MUSIC-X) data. Journal of Engineering Mechanics, 146(7), 04020061.
  [480] Ching, J., and Phoon, K. K. (2020). Measuring similarity between site-specific data and records from other sites. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 6(2), 04020011.
  [481] Ching, J., Huang, W. H., and Phoon, K. K. (2020). 3D probabilistic site characterization by sparse Bayesian learning. Journal of Engineering Mechanics, 146(12), 04020134.
  [482] Guan, Z., Wang, Y., Cao, Z., and Hong, Y. (2020). Smart sampling strategy for investigating spatial distribution of subsurface shallow gas pressure in Hangzhou Bay area of China. Engineering Geology, 274, 105711.
  [483] Guan, Z. and Wang, Y. (2020). Statistical charts for determining sample size at various levels of accuracy and confidence in geotechnical site investigation. Geotechnique, 70(12), 1145-1159.
  [484] Hu, Y. and Wang. Y. (2020). Probabilistic soil classification and stratification in a vertical cross-section from limited cone penetration tests using random field and Monte Carlo simulation. Computers and Geotechnics, 124, 103634.
  [485] Hu, Y., Wang, Y., Zhao, T., and Phoon, K. K. (2020). Bayesian supervised learning of site-specific geotechnical spatial variability from sparse measurements. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 6(2), 04020019.
  [486] Shuku, T., Phoon, K. K. and Yoshida, I. (2020). Trend estimation and layer boundary detection in depth-dependent soil data using sparse Bayesian lasso. Computers and Geotechnics, vol.128, 103845.
  [487] Wang, Y., Hu, Y., and Zhao, T. (2020). Cone penetration test (CPT)-based subsurface soil classification and zonation in two-dimensional vertical cross section using Bayesian compressive sampling. Canadian Geotechnical Journal, 57(7), 947-958.
  [488] Wang, Y., Hu, Y., and Zhao, T. (2020). CPT-based subsurface soil classification and zonation in a 2D vertical cross-section using Bayesian compressive sampling. Canadian Geotechnical Journal, 57(7), 947-958.
  [489] Xu, J., Zhang, L., Wang, Y., Wang, C., Zheng, J., and Yu, Y. (2020). Probabilistic estimation of cross-variogram based on Bayesian inference. Engineering Geology, 277, 105813.
  [490] Zhao, T., and Wang, Y. (2020). Interpolation and stratification of multilayer soil property profile from sparse measurements using machine learning methods. Engineering Geology, 265, 105430.
  [491] Zhao, T., Xu, L., and Wang, Y. (2020). Fast non-parametric simulation of 2D multi-layer cone penetration test (CPT) data without pre-stratification using Markov Chain Monte Carlo simulation. Engineering Geology, 273, 105670.
  [492] Zhao, T. and Wang, Y. (2020). Non-parametric simulation of non-stationary non-Gaussian 3D random field samples directly from sparse measurements using signal decomposition and Markov Chain Monte Carlo. Reliability Engineering & System Safety, 203, 107087.
  [493] Aladejare, A. E., Akeju, V. O., and Wang, Y. (2021). Probabilistic characterisation of uniaxial compressive strength of rock using test results from multiple types of punch tests. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 15(3), 209-220.
  [494] Asem, P., and Gardoni, P. (2021). A generalized Bayesian approach for prediction of strength and elastic properties of rock. Engineering Geology, 289, 106187.
  [495] Ching, J., Wu, S., and Phoon, K. K. (2021). Constructing quasi-site-specific multivariate probability distribution using hierarchical Bayesian model. Journal of Engineering Mechanics, 147(10), 04021069.
  [496] Ching, J., Phoon, K. K., Ho, Y. H., and Weng, M. C. (2021). Quasi-site-specific prediction for deformation modulus of rock mass. Canadian Geotechnical Journal, 58(7), 936-951.
  [497] Ching, J., Yang, Z., and Phoon, K. K. (2021). Dealing with Nonlattice Data in Three-Dimensional Probabilistic Site Characterization. Journal of Engineering Mechanics, 147(5), 06021003.
  [498] Guan, Z. and Wang, Y. (2021). Non-parametric construction of site-specific non-Gaussian multivariate joint probability distribution from sparse measurements. Structural Safety, 91, 102077.
  [499] Guan, Z. and Wang, Y. (2021). Selection of standard penetration test number for geotechnical investigation of a vertical cross-section considering spatial variability and correlation in soil properties. Canadian Geotechnical Journal, https://doi.org/10.1139/cgj-2020-0440.
  [500] Guan, Z. and Wang, Y. (2021). Rational determination of cone penetration test quantity in a two-dimensional vertical cross-section for site investigation. Tunnelling and Underground Space Technology, 109, 103771.
  [501] Han, L., Wang, L., Zhang, W., and Chen, Z. (2021). Quantification of statistical uncertainties of unconfined compressive strength of rock using Bayesian learning method. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1-16.
  [502] Lo, M. K., Wei, X., Chian, S. C., and Ku, T. (2021). Bayesian Network Prediction of Stiffness and Shear Strength of Sand. Journal of Geotechnical and Geoenvironmental Engineering, 147(5), 04021020.
  [503] Phoon, K. K., Ching, J., and Shuku, T. (2021). Challenges in data-driven site characterization. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1-13.
  [504] Phoon, K. K., and Ching, J. (2021). Project DeepGeo—Data-driven 3D subsurface mapping. Journal of GeoEngineering, 16(2), 47-59.
  [505] Shuku, T., and Phoon, K. K. (2021). Three-dimensional subsurface modeling using Geotechnical Lasso. Computers and Geotechnics, 133, 104068.
  [506] Wang, Y., and Li, P. (2021). Data-driven determination of sample number and efficient sampling locations for geotechnical site investigation of a cross-section using Voronoi diagram and Bayesian compressive sampling. Computers and Geotechnics, 130, 103898.
  [507] Wang, Y., Shi, C., and Li, X. (2021). Machine learning of geological details from borehole logs for development of high-resolution subsurface geological cross-section and geotechnical analysis. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1-19.
  [508] Wang, Y., Hu, Y., and Phoon, K. K. (2021). Non-parametric modelling and simulation of spatiotemporally varying geo-data. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, https://doi.org/10.1080/17499518.2021.1971258.
  [509] Xiao, S., Zhang, J., Ye, J., and Zheng, J. (2021). Establishing region-specific N–Vs relationships through hierarchical Bayesian modeling. Engineering Geology, 287, 106105.
  [510] Xu, J., Wang, Y., and Zhang, L. (2021). Interpolation of extremely sparse geo-data by data fusion and collaborative Bayesian compressive sampling. Computers and Geotechnics, 134, 104098.
  [511] Xu, J., Zhang, L., Li, J., Cao, Z., Yang, H., and Chen, X. (2021). Probabilistic estimation of variogram parameters of geotechnical properties with a trend based on Bayesian inference using Markov chain Monte Carlo simulation. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 15(2), 83-97.
  [512] Yoshida, I. and Shuku, T. (2021). Soil stratification and spatial variability estimated using sparse modeling and Gaussian random field theory. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7(3), 04021023.
  [513] Yoshida, I, Tomizawa, Y., and Otake, Y. (2021). Estimation of trend and random components of conditional random field using Gaussian process regression. Computers and Geotechnics, vol.136, 2021. https://doi.org/10.1016/j.compgeo.2021.104179.
  [514] Yoshida, I, Tasaki, Y., and Tomizawa, Y. (2021). Optimal placement of sampling locations for identification of a two-dimensional space. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1-16
  [515] Zhao, T., Wang, Y., and Xu, L. (2021). Efficient CPT locations for characterizing spatial variability of soil properties within a multilayer vertical cross-section using information entropy and Bayesian compressive sensing. Computers and Geotechnics, 137, 104260.
  [516] Zhang, P., Yin, Z. Y., and Jin, Y. F. (2021). Bayesian Neural Network-based Uncertainty Modelling: Application to Soil Compressibility and Undrained Shear Strength Prediction. Canadian Geotechnical Journal. https://doi.org/10.1139/cgj-2020-0751
  [517] Zhao, T. and Wang, Y. (2021). Statistical interpolation of spatially varying but sparsely measured 3D geo-data using compressive sensing and variational Bayesian inference. Mathematical Geosciences, 53, 1171–1199.
  [518] Zhang, D. M., Zhou, Y. L., Phoon, K. K., and Huang, H. W. (2020). Multivariate probability distribution of Shanghai clay properties. Engineering geology. 273, 105675.
  [519] Zhou, Y. L., Zhang, D. M., and Huang, H. W. (2019). Constructing a Multivariate Distribution of Clay Parameters Based on the Shanghai Database. Proceedings of the 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019)
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  15 Bayesian machine learning
  
  15.2 Pile Capacity
  [520] Baecher, G. R. and Rackwitz, R. (1982). Factors of safety and pile load tests. International Journal for Numerical and Analytical Methods in Geomechanics, 6(4), 609-624.
  [521] Najjar, S. and Gilbert, R. (2009). Importance of Lower-Bound Capacities in the Design of Deep Foundations. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 135(7), 890-900.
  [522] Park, J. H., Kim, D. and Chung, C. K. (2012). Implementation of Bayesian theory on LRFD of axially loaded driven piles. Computers and Geotechnics, 42, 73-80.
  [523] Papaioannou, I. and Straub, D. (2012). Reliability updating in geotechnical engineering including spatial variability of soil. Computers and Geotechnics, 42, 44-51.
  [524] Xu, D. S., Liu, Z. W., Chen, B., and Xu, X. Y. (2020). Bearing capacity analysis of offshore pipe piles with CPTs by considering uncertainly. Computers and Geotechnics, 126, 103731.
  [525] Zhang, J., Xiao, S. H., Huang, H. W., and Zhou, J. K. (2020). Calibrating a standard penetration test based method for region-specific liquefaction potential assessment. Bulletin of Engineering Geology and the Environment, 79(10), 5185-5204. https://doi.org/10.1007/s10064-020-01815-w.
  
  15.3 Liquefaction
  [526] Juang, C. H., Chen, C. J., Rosowsky, D. V. and Tang, W. H. (2000). CPT-based liquefaction analysis. Part 2. Reliability for design. Geotechnique, 50(5), 593-599.
  [527] Huang, H. W., Zhang, J., and Zhang, L. M. (2012). Bayesian network for characterizing model uncertainty of liquefaction potential evaluation models. KSCE Journal of Civil Engineering, 16(5), 714-722.
  [528] Wang, Y., Fu, C., and Huang, K. (2017). Probabilistic assessment of liquefiable soil thickness considering spatial variability and model and parameter uncertainties. Geotechnique, 67(3), 228-241.
  [529] Hu, J., and Liu, H. (2019). Bayesian network models for probabilistic evaluation of earthquake-induced liquefaction based on CPT and Vs databases. Engineering Geology, 254, 76-88.
  [530] Hu, J., and Liu, H. (2019). Identification of ground motion intensity measure and its application for predicting soil liquefaction potential based on the Bayesian network method. Engineering Geology, 248, 34-49.
  [531] Guan, Z., Wang, Y., and Zhao, T. (2021). Delineating the spatial distribution of soil liquefaction potential in a cross-section from limited cone penetration tests. Soil Dynamics and Earthquake Engineering, 145, 106710.
  [532] Schmidt, J., and Moss, R. (2021). Bayesian hierarchical and measurement uncertainty model building for liquefaction triggering assessment. Computers and Geotechnics, 132, 103963.
  [533] Guan, Z., and Wang, Y. (2022). CPT-based probabilistic liquefaction assessment considering soil spatial variability, interpolation uncertainty and model uncertainty. Computers and Geotechnics, 141, 104504.
  
  15.4 Embankments
  [534] Honjo, Y., Liu, W. T., and Soumitra, G. (1994). Inverse analysis of an embankment on soft clay by extended Bayesian method. International Journal of Numerical and Analytical Methods in Geomechanics, 18, 709-734.
  [535] Wu, T. H., Zhou, S. Z. and Gale, S. M. (2007). Embankment on sludge: predicted and observed performances. Canadian Geotechnical Journal, 44, 545-563.
  [536] Schweckendiek, T., Vrouwenvelder, A.C.W.M. and Calle, E.O.F. (2014). Updating piping reliability with field performance observations. Structural Safety, 47, 13-23.
  [537] Zheng, D., Huang, J., Li, D. Q., Kelly, R., and Sloan, S. W. (2018). Embankment prediction using testing data and monitored behaviour: A Bayesian updating approach. Computers and Geotechnics, 93, 150-162.
  [538] Zhou, W. H., Tan, F., and Yuen, K. V. (2018). Model updating and uncertainty analysis for creep behavior of soft soil. Computers and Geotechnics, 100, 135-143.
  [539] Huang, J., Zeng, C., and Kelly, R. (2019). Back analysis of settlement of Teven Road trial embankment using Bayesian updating. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 13(4), 320-325.
  [540] Lo, M. K., and Leung, Y. F. (2019). Bayesian updating of subsurface spatial variability for improved prediction of braced excavation response. Canadian Geotechnical Journal, 56(8), 1169-1183.
  [541] Rahimi, M., Shafieezadeh, A., Wood, D., Kubatko, E. J., and Dormady, N. C. (2019). Bayesian calibration of multi-response systems via multivariate Kriging: Methodology and geological and geotechnical case studies. Engineering Geology, 260, 105248.
  [542] Dassanayake, S. M., and Mousa, A. (2020). Probabilistic stability evaluation for wildlife-damaged earth dams: a Bayesian approach. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 14(1), 41-55.
  [543] Bozorgzadeh, N., and Bathurst, R. J. (2021). A Bayesian approach to reliability of MSE walls. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 15(1), 1-11.
  [544] Jin, Y., Biscontin, G., and Gardoni, P. (2021). Adaptive prediction of wall movement during excavation using Bayesian inference. Computers and Geotechnics, 137, 104249.
  [545] Spross, J., and Larsson, S. (2021). Probabilistic observational method for design of surcharges on vertical drains. Géotechnique, 71(3), 226-238.
  [546] Tao, Y. Q., Sun, H. L., and Cai, Y. Q. (2021). Bayesian inference of spatially varying parameters in soil constitutive models by using deformation observation data. International Journal for Numerical and Analytical Methods in Geomechanics, 45:1647-1663.
  [547] Van der Krogt, M. G., Schweckendiek, T., and Kok, M. (2021). Improving dike reliability estimates by incorporating construction survival. Engineering Geology, 280, 105937.
  
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  15 Bayesian machine learning
  
  15.5 Slope Stability and Landslides
  [548] Luckman, P. G., Der Kiureghian, A., and Sitar, N. (1987). Use of stochastic stability analysis for Bayesian back calculation of pore pressures acting in a cut slope at failure. Proc., in 5th International Conf. on Application of Statistics and Probability in Soil and Struct. Engr., Vancouver, Canada.
  [549] Reddi, L. N., and Wu, T. H. (1991). Probabilistic analysis of ground-water levels in hillside slopes. ASCE Journal of Geotechnical Engineering, 117(6), 872-890.
  [550] Cheung, R. W. M. and Tang, W. H. (2005). Realistic assessment of slope reliability for effective landslide hazard management. Geotechnique, 55(1), 85 –94.
  [551] Zhang, J., Zhang, L. M., and Tang, W. H. (2009). Bayesian framework for characterizing geotechnical model uncertainty. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 135(7), 932--940.
  [552] Zhang, L. L., Tang, W, H., and Zhang, L. M. (2009). Bayesian model calibration using geotechnical centrifuge tests. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 135(2): 291-299.
  [553] Zhang, J., Tang, W. H., and Zhang, L. M. (2010). Efficient probabilistic back analysis of slope stability parameters. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 136(1), 99-109.
  [554] Zhang, L. L., Zhang, J., Zhang, L. M., and Tang, W. H. (2010). Back analysis of slope failure with Markov chain Monte Carlo simulation. Computers and Geotechnics, 37(7-8), 905-912
  [555] Wang, Y., Cao, Z. J., and Au, S. K. (2010). Efficient Monte Carlo Simulation of parameter sensitivity in probabilistic slope stability analysis. Computers and Geotechnics, 37(7-8), 1015-1022.
  [556] Samui, P., Lansivaara, T., and Kim, D. (2011). Utilization relevance vector machine for slope reliability analysis. Applied Soft Computing, 11(5), 4036-4040.
  [557] Chiu, C. F., Yan, W. M. and Yuen, K. V. (2012). Reliability analysis of soil-water characteristics curve and its application to slope stability analysis. Engineering Geology, 135-136, 83-91.
  [558] Zhang, J., Tang, W. H., Zhang, L. M. and Huang, H. W. (2012). Characterising geotechnical model uncertainty by hybrid Markov Chain Monte Carlo simulation. Computers and Geotechnics, 43(6), 26-36.
  [559] Hasan, S. and Najjar, S. (2013). Probabilistic back analysis of failed slopes using Bayesian techniques. Geo-Congress 2013: Stability and Performance of Slopes and Embankments III (GSP 231), ASCE, Reston, VA, 1013-1022.
  [560] Zhang, L. L., Zuo, Z. B., Ye, G. L., Jeng, D. S. and Wang, J. H. (2013). Probabilistic parameter estimation and predictive uncertainty based on field measurements for unsaturated soil slope. Computers and Geotechnics, 48, 72-81.
  [561] Ranalli, M., Medina-Cetina, Z., Gottardi, G. and Nadim, F. (2014). Probabilistic calibration of a dynamic model for predicting rainfall-controlled landslides. ASCE Journal of Geotechnical and Geoenvironmental Engineering, 140(4), 04013039.
  [562] Zhang, J., Huang, H. W., Juang, C. H., and Su, W. W. (2014). Geotechnical reliability analysis with limited data: consideration of model selection uncertainty. Engineering Geology, 181, 27-37.
  [563] Zhang, L. L., Zheng, Y. F., Zhang, L. M., Li, X., and Wang, J. H. (2014). Probabilistic model calibration for soil slope under rainfall: effects of measurement duration and frequency in field monitoring. Geotechnique. 64(5), 365 –378
  [564] Li, X. Y., Zhang, L. M, and Zhang, S. (2018). Efficient Bayesian networks for slope safety evaluation with large quantity monitoring information Geoscience Frontiers, 10.1016/j.gsf.2017.09.009.
  [565] Jiang, S. H., Papaioannou, I., and Straub, D. (2018). Bayesian updating of slope reliability in spatially variable soils with in-situ measurements. Engineering Geology, 239, 310-320.
  [566] Li, D. Q., Zhang, F. P., Cao, Z. J., Tang, X. S., and Au, S. K. (2018). Reliability sensitivity analysis of geotechnical monitoring variables using Bayesian updating. Engineering Geology, 245, 130-140.
  [567] Yang, H. Q., Zhang, L., and Li, D. Q. (2018). Efficient method for probabilistic estimation of spatially varied hydraulic properties in a soil slope based on field responses: a bayesian approach. Computers and Geotechnics, 102, 262-272.
  [568] Zhang, L., Wu, F., Zheng, Y., Chen, L., Zhang, J., and Li, X. (2018). Probabilistic calibration of a coupled hydro-mechanical slope stability model with integration of multiple observations. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 12(3), 169-182.
  [569] Li, D. Q., Wang, L., Cao, Z. J., and Qi, X. H. (2019). Reliability analysis of unsaturated slope stability considering SWCC model selection and parameter uncertainties. Engineering Geology, 260, 105207.
  [570] Chivatá Cárdenas, I. (2019). On the use of Bayesian networks as a meta-modelling approach to analyse uncertainties in slope stability analysis. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 13(1), 53-65.
  [571] Sun, Y., Huang, J., Jin, W., Sloan, S. W., and Jiang, Q. (2019). Bayesian updating for progressive excavation of high rock slopes using multi-type monitoring data. Engineering Geology, 252, 1-13.
  [572] Wang, H. J., Xiao, T., Li, X. Y., Zhang, L. L., and Zhang, L. M. (2019). A novel physically-based model for updating landslide susceptibility. Engineering Geology, 251, 71-80.
  [573] Zhang, S., Li, C., Zhang, L., Peng, M., Zhan, L., and Xu, Q. (2020). Quantification of human vulnerability to earthquake-induced landslides using Bayesian network. Engineering Geology, 265, 105436.
  [574] Depina, I., Oguz, E. A., and Thakur, V. (2020). Novel Bayesian framework for calibration of spatially distributed physical-based landslide prediction models. Computers and Geotechnics, 125, 103660.
  [575] Jiang, S. H., Huang, J., Qi, X. H., and Zhou, C. B. (2020). Efficient probabilistic back analysis of spatially varying soil parameters for slope reliability assessment. Engineering Geology, 271, 105597.
  [576] Kool, J. J., Kanning, W., Jommi, C., and Jonkman, S. N. (2020). A Bayesian hindcasting method of levee failures applied to the Breitenhagen slope failure. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1-18.
  [577] Wang, L., Tang, L., Wang, Z., Liu, H., and Zhang, W. (2020). Probabilistic characterization of the soil-water retention curve and hydraulic conductivity and its application to slope reliability analysis. Computers and Geotechnics, 121, 103460.
  [578] Liu, X., and Wang, Y. (2021). Bayesian selection of slope hydraulic model and identification of model parameters using monitoring data and subset simulation. Computers and Geotechnics, 139, 104428.
  [579] Liu, X., and Wang, Y. (2021). Reliability analysis of an existing slope at a specific site considering rainfall triggering mechanism and its past performance records. Engineering Geology, 288, 106144.
  [580] Sun, X., Zeng, P., Li, T., Wang, S., Jimenez, R., Feng, X., and Xu, Q. (2021). From probabilistic back analyses to probabilistic run-out predictions of landslides: A case study of Heifangtai terrace, Gansu Province, China. Engineering Geology, 280, 105950.
  [581] Tian, H. M., Li, D. Q., Cao, Z. J., Xu, D. S., and Fu, X. Y. (2021). Reliability-based monitoring sensitivity analysis for reinforced slopes using BUS and subset simulation methods. Engineering Geology, 293, 106331.
  [582] Zeng, P., Sun, X., Xu, Q., Li, T., and Zhang, T. (2021). 3D probabilistic landslide run-out hazard evaluation for quantitative risk assessment purposes. Engineering Geology, 293, 106303.
  [583] Zhao, T., Lei, J., and Xu, L. (2021). An efficient Bayesian method for estimating runout distance of region-specific landslides using sparse data. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1-14.
  [584] Luo, J., Zhang, L., Yang, H., Wei, X., Liu, D., and Xu, J. (2021). Probabilistic model calibration of spatial variability for a physically-based landslide susceptibility model. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, DOI: 10.1080/17499518.2021.1988986.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  15 Bayesian machine learning
  
  15.6 Tunnels and Underground Openings
  [585] Ledesma, A., Gens, A., and Alonso, E. E. (1996). Parameter and variance estimation in geotechnical back analysis using prior information. International Journal of Numerical and Analytical Methods in Geomechanics, 20, 114-141.
  [586] Lee, M., Kim, D. H., and Lo, K. Y. (1997). A statistical approach on geotechnical parameter estimation for underground structures. In Proc. 9th International Conf. Computer Methods and Advances in Geomechanics, Wuhan, 775-780.
  [587] Lee, I. M., and Kim, D. H. (1999). Parameter estimation using extended Bayesian method in tunneling. Computers and Geotechnics, 24(2), 109-124.
  [588] Wang, L., Ravichandran, N., and Juang, C. H. (2012). Bayesian updating of KJHH model for prediction of maximum ground settlement in braced excavations in clays. Computers and Geotechnics, 44, 1-8.
  [589] Spačková, O. and Straub, D. (2013). Dynamic Bayesian network for probabilistic modeling of tunnel excavation processes. Computer-Aided Civil and Infrastructure Engineering 28(1): 1-21.
  [590] Janda, T., Šejnoha, M., and Šejnoha, J. (2018). Applying Bayesian approach to predict deformations during tunnel construction. International Journal for Numerical and Analytical Methods in Geomechanics, 42(15), 1765-1784.
  [591] Xie, X., Huang, H. W., Zhang, D. M., Liu Z. Q., and Lacasse S. (2020). Data-Fusion Based Vulnerability Analysis of Shield Driven Tunnel Suffering from Extreme Soil Surcharging. Proceedings of the 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019)
  [592] Zhang, G. H., Chen, W., Jiao, Y. Y., Wang, H., and Wang, C. T. (2020). A failure probability evaluation method for collapse of drill-and-blast tunnels based on multistate fuzzy Bayesian network. Engineering Geology, 276, 105752.
  [593] Xie X., Zhang, D. M., Huang, H. W., and Zhou, M. L. (2021). Data Fusion–Based Dynamic Diagnosis for Structural Defects of Shield Tunnel. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7(2): 04021019.
  [594] Sheil, B. B., Suryasentana, S., Templeman, J. O., Phillips, B. M., Cheng, W. C., and Zhang, L. (2022). Prediction of pipe jacking forces using a Bayesian updating approach. Journal of Geotechnical and Geoenvironmental Engineering, 148(1): 04021173.
  

• PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING
  

  16 Ensemble learning
  
  16.1 Site Characterization
  [595] Zhang, W., Wu, C., Zhong, H., Li, Y., and Wang, L. (2021). Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geoscience Frontiers, 12(1), 469-477.
  
  16.2 Piles
  [596] Zhang, W., Wu, C., Li, Y., Wang, L., and Samui, P. (2021). Assessment of pile drivability using random forest regression and multivariate adaptive regression splines. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 15(1), 27-40.
  
  16.3 Settlement of Foundations
  [597] Zhang, D.M., Zhang, J.Z., Huang, H.W., Qi, C.C., and Chang, C.Y. (2020) Machine learning-based prediction of soil compression modulus with application of 1D settlement. Journal of Zhejiang University. A. Science 21, 430-444.
  
  16.4 Slope Stability
  [598] Wang, L., Wu, C., Tang, L., Zhang, W., Lacasse, S., Liu, H., and Gao, L. (2020). Efficient reliability analysis of earth dam slope stability using extreme gradient boosting method. Acta Geotechnica, 15(11), 3135-3150.
  
  16.5 Tunnels and Underground Openings
  [599] Zhang, W., Zhang, R., Wu, C., Goh, A. T., and Wang, L. (2020). Assessment of basal heave stability for braced excavations in anisotropic clay using extreme gradient boosting and random forest regression. Underground Space, https://doi.org/10.1016/j.undsp.2020.03.001.
  [600] Zhang, R., Wu, C., Goh, A. T., Böhlke, T., and Zhang, W. (2021). Estimation of diaphragm wall deflections for deep braced excavation in anisotropic clays using ensemble learning. Geoscience Frontiers, 12(1), 365-373.
  [601] Ray, R., Kumar, D., Samui, P., Roy, L. B., Goh, A. T. C., and Zhang, W. (2021). Application of soft computing techniques for shallow foundation reliability in geotechnical engineering. Geoscience Frontiers, 12(1), 375-383.
  

• PART IV: PERFORMANCE COMPARISON OF MACHINE LEARNING ALGORITHMS USING THE SAME DATASET
  

  17 Performance comparison of ML algorithms
  
  17.1 Site Characterization
  [602] Rezaee, M., Mojtahedi, S. F. F., Taherabadi, E., Soleymani, K., and Pejman, M. (2020). Prediction of shear strength parameters of hydrocarbon contaminated sand based on machine learning methods. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1-19. (Multilayer perceptron, support vector machine, random forest, gradient boosting method, and multi-output support vector machine)
  [603] Shi, C. and Wang, Y. (2021). Non-parametric machine learning methods for interpolation of spatially varying non-stationary and non-Gaussian geotechnical properties. Geoscience Frontiers, 12, 339-350. (Ensemble radial basis function network, Bayesian compressive sensing, Multiple point statistics)
  
  17.2 Piles
  [604] Zhang, W., Wu, C., Li, Y., Wang, L., and Samui, P. (2021). Assessment of pile drivability using random forest regression and multivariate adaptive regression splines. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 15(1), 27-40. (Random forest regression, and multivariate adaptive regression splines)
  
  17.3 Liquefaction
  [605] Samui, P., and Sitharam, T. G. (2011). Machine learning modelling for predicting soil liquefaction susceptibility. Natural Hazards and Earth System Sciences, 11(1), 1-9. (Artificial neural network, Support vector machine)
  [606] He, X., Xu, H., Sabetamal, H., and Sheng, D. (2020). Machine learning aided stochastic reliability analysis of spatially variable slopes. Computers and Geotechnics, 126, 103711. (Artificial neural network, Support vector regression)
  [607] Duan, W., Congress, S. S. C., Cai, G., Liu, S., Dong, X., Chen, R., and Liu, X. (2021). A hybrid GMDH neural network and logistic regression framework for state parameter-based liquefaction evaluation. Canadian Geotechnical Journal. https://doi.org/10.1139/cgj-2020-0686. (Neural networks, and logistic regression)
  
  17.4 Soil Retailing Walls
  [608] Liu, D., Lin, P., Zhao, C., and Qiu, J. (2021). Mapping horizontal displacement of soil nail walls using machine learning approaches. Acta Geotechnica, 1-18. (Artificial neural network, random forest, and support vector machine)
  

• PART IV: PERFORMANCE COMPARISON OF MACHINE LEARNING ALGORITHMS USING THE SAME DATASET
  

  17 Performance comparison of ML algorithms
  
  17.5 Slope Stability and Landslide
  [609] Yao, X., Tham, L. G., and Dai, F. C. (2008). Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology, 101(4), 572-582. (One-class support vector machine, two-class support vector machine)
  [610] Marjanovic, M., Bajat, B., and Kovacevic, M. (2009). Landslide susceptibility assessment with machine learning algorithms. In Intelligent Networking and Collaborative Systems, 2009. INCOS'09. International Conference on (pp. 273-278). IEEE. (Support vector machine with Gaussian kernel and k-Nearest Neighbor algorithms)
  [611] Yilmaz, I. (2010). Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environmental Earth Sciences, 61(4), 821-836. (Conditional probability, logistic regression, artificial neural networks, and support vector machine)
  [612] Marjanović, M., Kovačević, M., Bajat, B., and Voženílek, V. (2011). Landslide susceptibility assessment using SVM machine learning algorithm. Engineering Geology, 123(3), 225-234. (Support vector machine, Decision tress, Logistic regression)
  [613] Tien Bui, D., Pradhan, B., Lofman, O., & Revhaug, I. (2012). Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and Naive Bayes Models. Mathematical Problems in Engineering, 20(5), 705–718. (Support vector machines, Decision tree, and Naive Bayes models)
  [614] Pradhan, B. (2013). A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers and Geosciences, 51, 350-365. (Decision tree, Support vector machine and adaptive neuro-fuzzy inference system (ANFIS))
  [615] Goetz, J. N., A. Brenning, H. Petschko, and P. Leopold. (2015). Evaluating Machine Learning and Statistical Prediction Techniques for Landslide Susceptibility Modeling. Computers and Geosciences, 81: 1–11. (Generalized logistic regression, generalized additive models, weights of evidence, support vector machine, random forest classification, and bootstrap aggregated classification trees (bundling) with penalized discriminant analysis)
  [616] Qi, C., and Tang, X. (2018). A hybrid ensemble method for improved prediction of slope stability. International Journal for Numerical and Analytical Methods in Geomechanics, 42(15), 1823-1839. (Gaussian process classification, quadratic discriminant analysis, support vector machine, artificial neural networks, adaptive boosted decision trees, and k-nearest neighbours)
  [617] Đurić, U., Marjanović, M., Radić, Z., and Abolmasov, B. (2019). Machine learning based landslide assessment of the Belgrade metropolitan area: Pixel resolution effects and a cross-scaling concept. Engineering Geology, 256, 23-38. (Support vector machines, and Random forest)
  [618] Liu, Z. Q., Guo, D., Lacasse, S., Li, J. H., Yang B. B., and Choi, J. C. (2020). Algorithms for intelligent prediction of landslide displacements. Journal of Zhejiang University-SCIENCE A, 21(6), 412-429. (Long-short term memory, random forest, gated recurrent unit)
  [619] Deng, L., Smith, A., Dixon, N., and Yuan, H. (2021). Machine learning prediction of landslide deformation behaviour using acoustic emission and rainfall measurements. Engineering Geology, 293, 106315. (Extreme learning machine, LASSO-ELM, support vector machine, and back propagation neural network)
  [620] Kainthura, P., and Sharma, N. (2021). Machine learning driven landslide susceptibility prediction for the Uttarkashi region of Uttarakhand in India. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1-14. (Random Forest, Artificial Neural Network, and Bayesian Network)
  [621] Sun, D., Xu, J., Wen, H., and Wang, D. (2021). Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: A comparison between logistic regression and random forest. Engineering Geology, 281, 105972. (Logistic regression, random forest)
  [622] Wang, H. J., Zhang, L., Luo, H., He, J., and Cheung, R. W. M. (2021). AI-powered landslide susceptibility assessment in Hong Kong. Engineering Geology, 288, 106103. (Logistic regression, random forest, LogitBoost, 2001convolutional neural network (CNN), bidi-rectional long short-term memory architecture of recurrent neural network (BiLSTM-RNN), and CNN-LSTM)
  [623] Wang, H. J., Zhang, L. M., Yin, K. S., Luo, H. Y., and Li, J. H. (2021). Landslide identification using machine learning. Geoscience Frontiers, 12(1), 351-364. (Logistic regression, support vector machine, random forest, boosting methods and convolutional neural network)
  [624] Gong, W., Hu, M., Zhang, Y., Tang, H., Liu, D., and Song, Q. (2021). GIS-based landslide susceptibility mapping using ensemble methods for Fengjie County in the Three Gorges Reservoir Region, China. International Journal of Environmental Science and Technology, 1-18. (Conventional statistical approaches, neural network, support vector machine, ensemble methods)
  
  17.6 Tunnels and Underground Openings
  [625] Pu, Y., Apel, D. B., and Hall, R. (2020). Using machine learning approach for microseismic events recognition in underground excavations: Comparison of ten frequently-used models. Engineering Geology, 268, 105519. (Support Vector Machine, Backpropagation Neural Network, Gaussian Process Classifier, Decision Tree, NaiveBayesian Classifier, k-Nearest Neighbor, LogisticRegression, AdaBoost, Random Forest, and Gradient boosting Classifier)
  [626] Zhang, W., Zhang, R., Wu, C., Goh, A. T. C., Lacasse, S., Liu, Z., and Liu, H. (2020). State-of-the-art review of soft computing applications in underground excavations. Geoscience Frontiers, 11(4), 1095-1106. (eXtreme Gradient Boosting, Multivariate Adaptive Regression Splines, Artificial Neural Networks, and Support Vector Machine)
  [627] Chen, J. Y., Huang, H. W., Cohn, A. G., Zhang, D. M., & Zhou, M. L. (2021). Machine learning-based classification of rock discontinuity trace: SMOTE oversampling integrated with GBT ensemble learning. International Journal of Mining Science and Technology. (Gradient boosting tree, Random forest, Decision tree, and Multiple layers perceptron)