Machine Learning-Based Modelling of Soil Properties for Geotechnical Design: Review, Tool Development and Comparison

Machine learning (ML) holds significant potential for predicting soil properties in geotechnical design but at the same time poses challenges, including those of how to easily examine the performance of an algorithm and how to select an optimal algorithm. This study first comprehensively reviewed the application of ML algorithms in modelling soil properties for geotechnical design. The algorithms were categorized into several groups based on their principles, and the main characteristics of these ML algorithms were summarized. After that six representative algorithms are further detailed and selected for the creation of a ML-based tool with which to easily build ML-based models. Interestingly, automatic determination of the optimal configurations of ML algorithms is developed, with an evaluation of model accuracy, application of the developed ML model to the new data and investigation of relationships between the input variables and soil properties. Furthermore, a novel ranking index is proposed for the model comparison and selection, which evaluates a ML-based model from five aspects. Soil maximum dry density is selected as an example to allow examination of the performance of different ML algorithms, the applicability of the tool and the model ranking index to determining an optimal model.

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Abbreviations

Bias vector of the ith hidden layer

Constant coefficient vector

Number of iterations

Output of the ith hidden layer

Number of datasets

Number of features at each node

Dimension of input variables

Dimension of transformed variables

Number of decision trees

Stochastic calculation times

Probability of crossover

Probability of mutation

Size of population

Bernoulli distribution with probability of p

Weight matrix of the ith hidden layer

Maximum value of the variable xi

Minimum value of the variable xi

Normalized value of a dataset

Matrix of input variables

Matrix of transformed variables

Actual value of the output variable

Predicted value of the output variable

Mean value of the actual output variable

Output of the output layer

Slack parameter (default value: 0.1).

Mean value of output

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Acknowledgements

This research was financially supported by the Research Grants Council (RGC) of Hong Kong Special Administrative Region Government (HKSARG) of China (Grant No.: 15220221, R5037-18F).