A Comparative Study of Soil Liquefaction Assessment Using Machine Learning Models

Liquefaction of saturated granular soils is marked by the total loss of shear strength of soil under dynamic cyclic or transient loading conditions due to excess pore water pressure that builds up to produce a soil regime that mechanically performs as a liquid. The cone penetration test (CPT) is widely recognized as a means of evaluating liquefaction susceptibility. This study presents a comparative supervised machine learning-based assessment for CPT-based liquefaction data. In particular, this study views soil liquefaction as a binary classification problem, whether the soil is liquefied or not, by utilizing three supervised machine learning classifiers: support vector machine, Decision Trees, and Quadratic Discrimination Analysis. To build the supervised machine learning models, three different soil characterization data sets were selected by performing CPTs at specific locations. The first input data (input data-1) is constructed as a function of the Mean Grain Size (D₅₀), Measured CPT Tip Resistance (q_c), Earthquake Magnitude (M), and Cyclic Shear Resistance (CSR). The second input data (input data-2) employed D₅₀, Normalized CPT Tip Resistance (q_c−1), M, CSR. Finally, the third input data (input data-3) consists of D₅₀, q_c−1, M, the Maximum Ground Acceleration (a_max), Effective Vertical Overburden Stress, and Total Overburden Stress. The significance feature analysis shows the most important feature for assessing liquefaction susceptibility in the soil using input data for model 1 is measured CPT Tip Resistance, for input data model 2 it is normalized CPT Tip Resistance, and finally, for input data model 3, it is measured CPT Tip Resistance. Conclusively, this study proposed simple and quick approaches to evaluate soil liquefaction susceptibility without complicated calculations.