Reference List for Machine Learning and its Applications in Geotechnical Engineering

(References are listed in chronological order)

Content

      
PART I: NEED TO KNOW KNOWLEDGE

      1 Probability Theory
      2 Information Theory
      3 Decision Theory
      4 Introductory Materials on Machine Learning

PART II: MACHINE LEARNING ALGORITHMS

      5 Supervised learning
            5.1 Decision tree learning
            5.2 Artificial neural networks
            5.3 Deep learning
            5.4 Inductive logic programming
            5.5 Support vector machines
            5.6 Similarity and metric learning
            5.7 Sparse dictionary learning
      6 Semi-supervised learning
      7 Reinforcement learning
      8 Unsupervised learning
            8.1 Clustering
            8.2 Feature learning (Dimensionality reduction)
            8.3 Outlier detection
            8.4 Generative adversarial networks
      9 Bayesian machine learning
            9.2 Bayesian neural network
            9.3 Gaussian processes
            9.4 Relevance vector machine (sparse Bayesian learning)
            9.5 Bayesian deep learning
            9.6 Bayesian model class selection and system identification
            9.7 Simulation-based methods for Bayesian inference

PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING

      10 Artificial neural networks
            10.1 Site Characterization
            10.2 Geomaterial Properties and Behavior Modeling
            10.3 Pile Capacity and Settlement
            10.4 Shallow Foundations
            10.5 Tensile Capacity of Anchors
            10.6 Liquefaction
            10.7 Soil Retaining Structures
            10.8 Slope Stability
            10.9 Tunnels and Underground Openings
            10.10 Ground Improvement
      11 Support vector machine
            11.1 Site Characterization
            11.2 Geomaterial Properties and Behavior Modeling
            11.3 Pile Capacity
            11.4 Settlement of Foundations
            11.5 Liquefaction
            11.6 Soil Retaining Walls and Dams
            11.7 Slope Stability
            11.8 Tunnels and Underground Openings
            11.9 Landslide
      12 Clustering
            12.1 Site Characterization
            12.2 Liquefaction
            12.3 Slope Stability
            12.4 Lifeline Engineering
            12.5 Landslide
      13 Feature learning (Dimensionality reduction)
            13.1 Soil Retaining Walls
            13.2 Slope Stability
            13.3 Tunnels and Underground Openings
            13.4 Landslide
            13.6 Others
      14 Outlier detection
            14.1 Site Characterization
            14.2 Others
      15 Bayesian machine learning
            15.1 Site Characterization
            15.2 Pile Capacity
            15.3 Liquefaction
            15.4 Embankments
            15.5 Slope Stability
            15.6 Tunnels and Underground Openings

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

      16 Performance comparison of ML algorithms
            16.1 Landslide
            16.2 Liquefaction