Predictive models for dynamic properties of soils using machine learning approaches: A comprehensive review
Samira Ghorbanzadeh, Danial Jahed Armaghani, Mahdi Salimi, Meghdad Payan
Abstract
Understanding soil shear stiffness (G) and damping ratio (D) is essential for evaluating geotechnical stability under dynamic loads like earthquakes and machine vibrations. These parameters are critical for assessing site response, designing foundations, and ensuring overall structural stability. Traditional methods, such as cyclic simple shear, cyclic triaxial, and resonant column tests, often face limitations due to inadequate site sampling, potential sampling errors, and difficulties in replicating field conditions in the laboratory. These challenges necessitate numerous tests, which in turn escalate costs related to equipment and labor. In response to these issues, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as promising alternatives. This paper reviews several ML techniques applied in the literature for predicting soil dynamic properties, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), tree-based models, Evolutionary Algorithms (EA), and Fuzzy Logic-based (FL) models. The analysis demonstrates that ML models not only provide enhanced accuracy (coefficient of determination, R 2 , up to 0.994) compared to traditional empirical equations (R 2 typically below 0.85) but also effectively capture the complex behavior of soils under dynamic conditions. The study also examines the advantages and limitations of current ML-based models, identifying key challenges and future research directions in predicting G and D. The findings demonstrate that ML techniques enhance prediction accuracy, reduce testing costs, and improve efficiency, making them valuable tools for geotechnical engineering applications. This review provides guidance for engineers and researchers in selecting suitable ML models and optimization strategies to improve the prediction of soil dynamic properties. • The review explores Machine learning (ML) techniques to predict soil G and D. • Comprehensive review assesses ML accuracy and scalability for geotechnical use. • The study highlights ML limits and future needs, like big data and tuning. • Results show ML surpasses empirical formulas in nonlinear soil response. • Advances in ML enhance geotechnical design through better soil prediction.