A Review on Application of Soft Computing Techniques in Geotechnical Engineering

Abstract

Numerous test results, mathematical relationships, and in-the-moment analysis and design are all components of geotechnical issues. Additionally, due to smart infrastructure and materials, the research trend in engineering nowadays is shifting toward intelligent tools and their ability to tackle engineering problems. Artificial neural networks (ANN), support vector machines (SVM), genetic algorithms (GA), and particle swarm optimization algorithms (PSO), among other soft computing techniques, have made significant progress in recent years in solving geotechnical issues. Based on a review of more than 800 published research, this study discusses the applicability of soft computing techniques in the current environment. Traditional methods, such as regression analysis and trial-and-error techniques, take time and could be more effective. Additionally, most geotechnical designs require considerable experimental data and may require laborious work. A novel methodology for soft computing approaches has emerged to solve the problems mentioned above. This paper presents soil problems and geotechnical challenges while examining recent developments and the potential applications of soft computing. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.