A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil

Mathematical Problems in Engineering - Tập 2021 - Trang 1-11 - 2021
Binh Thai Pham1,2, Manh Duc Nguyen3, Nadhir Al‐Ansari4, Quoc Anh Tran5, Lanh Si Ho1,2, Hiep Van Le2, Indra Prakash6
1Civil and Environmental Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan
2University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi 100000, Vietnam
3University of Transport and Communications, Hanoi 100000, Vietnam
4Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea 971 87, Sweden
5Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, Norway
6DDG (R) Geological Survey of India, Gandhinagar 382010, India

Tóm tắt

Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), which are well-known as advanced machine learning techniques, were used to predict the permeability coefficient (K) of soil (10−9 cm/s), based on a set of simple six input parameters such as natural water content w (%), void ratio (e), specific density (g/cm3), liquid limit (LL) (%), plastic limit (PL) (%), and clay content (%). For this, a total of 84 soil samples data collected from the detailed design stage investigations of Da Nang-Quang Ngai national road project in Vietnam was used to generate training (70%) and testing (30%) datasets for building and validating the models. Statistical error indicators such as RMSE and MAE and correlation coefficient (R) were used to evaluate and compare performance of the models. The results show that all the three models performed well (R > 0.8) for the prediction of permeability coefficient of soil, but the RF model (RMSE = 0.0084, MAE = 0.0049, and R = 0.851) is more efficient compared with the other two models, namely, ANN (RMSE = 0.001, MAE = 0.005, and R = 0.845) and SVM (RMSE = 0.0098, MAE = 0.0064, and R = 0.844). Thus, it can be concluded that the RF model can be used for accurate estimation of the permeability coefficient (K) of the soil.

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Mathematical Problems in Engineering

Tập 2021

1-11

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