The Application of Machine Learning Techniques in Geotechnical Engineering: A Review and Comparison

Wei Shao, Wenhan Yue, Ye Zhang, Tianxing Zhou, Yutong Zhang, Yabin Dang, Haoyu Wang, Xianhui Feng () and Zhiming Chao ()
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Wei Shao: College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
Wenhan Yue: College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
Ye Zhang: Mentverse Ltd., 25 Cabot Square, Canary Wharf, London E14 4QZ, UK
Tianxing Zhou: Mentverse Ltd., 25 Cabot Square, Canary Wharf, London E14 4QZ, UK
Yutong Zhang: Mentverse Ltd., 25 Cabot Square, Canary Wharf, London E14 4QZ, UK
Yabin Dang: College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
Haoyu Wang: College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
Xianhui Feng: School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China
Zhiming Chao: College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 201306, China

Mathematics, 2023, vol. 11, issue 18, 1-16

Abstract: With the development of data collection and storage capabilities in recent decades, abundant data have been accumulated in geotechnical engineering fields, providing opportunities for the usage of machine learning approaches. Thus, a rising number of scholars are adopting machine learning techniques to settle geotechnical issues. In this paper, the application of three popular machine learning algorithms, support vector machine (SVM), artificial neural network (ANN), and decision tree (DT), as well as other representative algorithms in geotechnical engineering, is reviewed. Meanwhile, the applicability of diverse machine learning algorithms in settling specific geotechnical engineering issues is compared. The main findings are as follows: ANN, SVM, and DT have been widely adopted to solve a variety of geotechnical engineering issues, such as the classification of soil and rock types, predicting the properties of geotechnical materials, etc. Based on the collected relevant research, the performance of random forest (RF) in sorting soil types and assessing landslide susceptibility is satisfying; SVM has high precision in classifying rock types and forecasting rock deformation; and backpropagation ANNs and Hopfield ANNs are recommended to forecast rock compressive strength and soil settlement, respectively.

Keywords: geotechnical engineering; rock; soil line (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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