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REVIEW article

Front. Built Environ.
Sec. Geotechnical Engineering
Volume 10 - 2024 | doi: 10.3389/fbuil.2024.1373092

A Scientometrics Review of Conventional and Soft Computing Methods in the Slope Stability Analysis

Provisionally accepted
Feezan Ahmad Feezan Ahmad 1*Xiao-Wei Tang Xiao-Wei Tang 2*Mahmood Ahmad Mahmood Ahmad 3Taoufik Najeh Taoufik Najeh 4*Yaser Gamil Yaser Gamil 5
  • 1 Dalian University of Technology, Dalian, China
  • 2 Graduate School, Dalian University of Technology, Dalian, Liaoning Province, China
  • 3 University of Engineering and Technology, Peshawar, Peshawar, Khyber Pakhtunkhwa, Pakistan
  • 4 Department of Engineering Sciences and Mathematics, Luleå University of Technology, Lulea, Sweden
  • 5 Monash University Malaysia, Subang Jaya, Selangor, Malaysia

The final, formatted version of the article will be published soon.

    Predicting slope stability is important for preventing and mitigating landslide disasters. This paper examines the existing approaches for analyzing slope stability. There are several established conventional approaches for slope stability analysis that can be applied in this context. However, in recent decades, soft computing methods has been extensively developed and employed in stochastic slope stability analysis, notably as surrogate models to improve computing efficiency in contrast to traditional approaches. Soft computing methods can deal with uncertainty and imprecision, which may be quantified using performance indices like coefficient of determination, in regression and accuracy in classification. This review study focuses on conventional methods such as the Bishop's method and Janbu's method, as well as soft computing models such as support vector machine, artificial neural network, Gaussian process regression, decision tree, etc. The advantages and limitations of soft computing techniques in relation to conventional methods have also been thoroughly covered in this paper. The achievements of soft computing methods are summarized from two aspects-predicting factor of safety and classification of slope stability. Key potential research challenges and future prospects are also given.

    Keywords: slope stability, Conventional methods, Soft computing methods, stochastic analysis, Performance metrics

    Received: 19 Jan 2024; Accepted: 09 Sep 2024.

    Copyright: © 2024 Ahmad, Tang, Ahmad, Najeh and Gamil. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Feezan Ahmad, Dalian University of Technology, Dalian, China
    Xiao-Wei Tang, Graduate School, Dalian University of Technology, Dalian, 116024, Liaoning Province, China
    Taoufik Najeh, Department of Engineering Sciences and Mathematics, Luleå University of Technology, Lulea, 97187, Sweden

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.