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
             9.1 Bayesian network
            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 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
             11.10 Railway track
       12 Clustering
             12.1 Site Characterization
             12.2 Liquefaction
             12.3 Slope Stability
             12.4 Lifeline Engineering
             12.5 Landslide
             12.6 Tunnels and Underground Openings
       13 Feature learning (Dimensionality reduction)
             13.1 Soil Retaining Walls
             13.2 Slope Stability
             13.3 Tunnels and Underground Openings
             13.4 Landslide
            13.5 Offshore Engineering
             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
       16 Ensemble learning
             16.1 Site Characterization
             16.2 Pile
             16.3 Settlement of Foundations
             16.4 Slope Stability
             16.5 Tunnels and Underground Openings

PART IV: PERFORMANCE COMPARISON OF MACHINE LEARNING ALGORITHMS USING THE SAME DATASET
      17 Performance comparison of ML algorithms
             17.1 Site Characterizatione
             17.2 Pile
             17.3 SLiquefaction
             17.4 Soil Retailing Walls
             17.5 Slope Stability and Landslide
             17.6 Tunnels and Underground Openings