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Artificial intelligence algorithms for predicting peak shear strength of clayey soil-geomembrane interfaces and experimental validation
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Chao, Zhiming, Shi, Danda, Fowmes, Gary J., Xu, Xu, Yu, Wenhan, Cui, Peng, Hu, Tianxiang and Yang, Chuanxin (2023) Artificial intelligence algorithms for predicting peak shear strength of clayey soil-geomembrane interfaces and experimental validation. Geotextiles and Geomembranes, 51 (1). pp. 179-198. doi:10.1016/j.geotexmem.2022.10.007 ISSN 0266-1144.
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Official URL: https://doi.org/10.1016/j.geotexmem.2022.10.007
Abstract
The peak shear strength of clayey soil-geomembrane interfaces is a vital parameter for the design of relevant engineering infrastructure. However, due to the large number of influence factors and the complex action mechanism, accurate prediction of the peak shear strength for clayey soil-geomembrane interfaces is always a challenge. In this paper, a machine learning model was established by combining Mind Evolutionary Algorithm (MEA) and the ensemble algorithm of Adaptive Boosting Algorithm (ADA)-Back Propagation Artificial Neural Network (BPANN) to predict the peak shear strength of clayey soil-geomembrane interfaces based on the results of 623 laboratory interface direct shear experiments. By comparing with the conventional machine learning algorithms, including Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA) tuned ADA-BPANN, MEA tuned Support Vector Machine (SVM) and Random Forest (RF), the superior performance of MEA tuned ADA-BPANN has been validated, with higher predicting precision, shorter training time, and the avoidance of local optimum and overfitting. By adopting the proposed novel model, sensitivity analysis was carried out, which indicates that normal pressure has the largest influence on the peak shear strength, followed by geomembrane roughness. Furthermore, an analytical equation was proposed to assess the peak shear strength that allows the usage of machine learning skills for the practitioners with limited machine learning knowledge. The present research highlights the potential of the MEA tuned ADA-BPANN model as a useful tool to assist in preciously estimating the peak shear strength of clayey soil-geomembrane interfaces, which can provide benefits for the design of relevant engineering applications.
Item Type: | Journal Article | |||||||||
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Subjects: | T Technology > TA Engineering (General). Civil engineering (General) | |||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||
Library of Congress Subject Headings (LCSH): | Geomembranes, Machine learning, Algorithms, Artificial intelligence -- Industrial applications, Geotechnical engineering, Engineering geology | |||||||||
Journal or Publication Title: | Geotextiles and Geomembranes | |||||||||
Publisher: | Elsevier | |||||||||
ISSN: | 0266-1144 | |||||||||
Official Date: | February 2023 | |||||||||
Dates: |
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Volume: | 51 | |||||||||
Number: | 1 | |||||||||
Page Range: | pp. 179-198 | |||||||||
DOI: | 10.1016/j.geotexmem.2022.10.007 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 3 October 2022 | |||||||||
Date of first compliant Open Access: | 29 October 2023 | |||||||||
RIOXX Funder/Project Grant: |
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