Application of support vector machine in geotechnical engineering: A review
Department of Civil Engineering, Maulana Azad National Institute of Technology, Bhopal, India.
Review Article
Global Journal of Engineering and Technology Advances, 2024, 21(03), 103-113.
Article DOI: 10.30574/gjeta.2024.21.3.0229
Publication history:
Received on 10 November 2024; revised on 16 December 2024; accepted on 18 December 2024
Abstract:
Support Vector Machine (SVM) is an advanced machine learning technique grounded in statistical theory, designed to address both linear and non-linear classification and regression problems. In recent years, SVM has gained significant traction in diverse domains of geotechnical engineering, such as foundation engineering, slope stability analysis, soil-structure interaction, and tunnel analysis. A thorough review of the existing literature reveals that SVM has been applied effectively across various sub-disciplines of geotechnical engineering, demonstrating its versatility and capability to handle complex data sets and problems that traditional methods struggle with.
Conventional geotechnical analysis techniques, including the finite element method, limit equilibrium method, and upper bound limit analysis, often fall short in dealing with the intricate, non-linear relationships present in soil-structure interactions. These traditional approaches are largely grounded in linear relationship frameworks, which limits their effectiveness in capturing the complexities of real-world geotechnical scenarios. This paper aims to provide an extensive review of the strengths, applicability, and diverse applications of SVM within the field of geotechnical engineering. It will explore how SVM can not only be utilized efficiently in existing methodologies but also be enhanced through integration with other optimization algorithms. By doing so, this review seeks to offer valuable insights into the potential improvements and innovative directions for SVM applications, paving the way for more robust solutions in geotechnical challenges.
Keywords:
Support Vector Machine; Slope Stability; Foundation; Machine Learning; Kernel Function; Radial Basis Function
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