Abstract
This study presents a comprehensive analysis of the capability of machine learning techniques in estimating the static liquefaction of sands containing plastic fines. In this regard, six methods, including backpropagation multi-layer perceptron, support vector regression (SVR), lazy K-star (LKS), decision table, random forest, and M5, are employed to predict the static liquefaction of saturated clayey sand. Static liquefaction susceptibility of soil is measured using the brittle index. The dataset includes 114 unconsolidated undrained triaxial shear tests performed on saturated sand containing various amounts of plastic fines. Results indicate that all employed models provide satisfactory predictions, with correlation coefficients ranging from 0.82 to 0.92 for testing set. Among all models, the SVR and LKS models make more accurate and reliable predictions. Furthermore, the significance of each input parameter is assessed through a series of sensitivity analyses, which shows that plasticity of fine particles, host sand gradation, and intergranular void ratio are more influential on static liquefaction. Additionally, some mathematical equations are presented for estimating the static liquefaction potential.
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Data Availability
The dataset for this study is available upon request from the corresponding author.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by ST. The first draft of the manuscript was written by ST and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Talamkhani, S., Naeini, S.A. & Ardakani, A. Prediction of Static Liquefaction Susceptibility of Sands Containing Plastic Fines Using Machine Learning Techniques. Geotech Geol Eng 41, 3057–3074 (2023). https://doi.org/10.1007/s10706-023-02444-2
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DOI: https://doi.org/10.1007/s10706-023-02444-2