Artificial intelligence-based predictive models for shear wave velocity of soils: A comprehensive review
Meghdad Payan, Parsa Asadi, Amirhossein Jamaldar, Mahdi Salimi, Payam Zanganeh Ranjbar, Danial Jahed Armaghani, Xuzhen He, Daichao Sheng
Abstract
Shear wave velocity (V s ) of soils is a crucial property in geotechnical engineering practice, affecting seismic site response analysis, seismic hazard assessment , and dynamic soil-structure interaction. The precise determination of V s is crucial in assessing the dynamic behavior of soils during seismic events, as it markedly influences the amplification and attenuation of ground motions. While several empirical equations have been proposed thus far for estimating V s in earthen materials, the majority of them lack the required accuracy and predictive capability. As a result, there has been a growing tendency among practicing engineers towards utilizing Artificial Intelligence (AI) for V s prediction. This paper presents an extensive overview of the developments in deploying AI and its subsets, including Machine Learning (ML) and Deep Learning (DL) techniques, for the precise estimation of V s in soil deposits. Notably, despite the importance of shear wave velocity as a key geotechnical parameter, no prior review study has exclusively focused on evaluating it using AI-based models. This review systematically examines various AI-based methodologies employed by researchers to enhance the reliability and precision of V s predictions using soil properties and in-situ test data. The advantages of ML techniques over conventional empirical correlations are thoroughly analyzed and critically compared. Additionally, the paper discusses the relative performance of different AI-based approaches, outlining their strengths and limitations in V s estimation. The review also provides a general qualitative assessment of V s measurement methods, offering guidance on selecting the most appropriate approach based on project-specific requirements and constraints. Finally, through a critical evaluation of existing literature, key knowledge gaps are identified, and potential directions for future research in this domain are proposed.