Article title: Prediction of shear strength of soft soil using machine learning methods
Journal information: CATENA (Netherlands) - ISI journal (IF, 2016 = 3.191)
First author: Binh Thai Pham, Co-Head of Geotechnical Engineering and Artificial Intelligence research group, University of Transport Technology, Viet Nam.
Corresponding author: Dieu Tien Bui, Head of Geotechnical Engineering and Artificial Intelligence research group, University of Transport Technology, Viet Nam; Assoc Prof, University College of Southeast Norway, Norway.
Co-authors: Le Hoang Son (GEOAI); Tuan Anh Hoang; Duc Manh Nguyen (GEOAI)
Shear strength of the soil is an important engineering parameter used in the design and audit of geo-technical structures. In this research, we aim to investigate and compare the performance of four machine learning methods, Particle Swarm Optimization - Adaptive Network based Fuzzy Inference System (PANFIS), Genetic Algorithm - Adaptive Network based Fuzzy Inference System (GANFIS), Support Vector Regression (SVR), and Artificial Neural Networks (ANN), for predicting the strength of soft soils. For this purpose, case studies of 188 plastic clay soil samples collected from two major projects, Nhat Tan and Cua Dai bridges in Viet Nam have been used for generating training and testing datasets for constructing and validating the models. Validation and comparison of the models have been carried out using RMSE, and R. The results show that the PANFIS has the highest prediction capability (RMSE=0.038 and R=0.601), followed by the GANFIS (RMSE=0.04 and R=0.569), SVR (RMSE=0.044 and R=0.549), and ANN (RMSE=0.059 and R=0.49). It can be concluded that out of four models the PANFIS indicates as a promising technique for prediction of the strength of soft soils.
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