Predicting the shear modulus and damping ratio of soils using machine learning models

Abstract

In geotechnical engineering, the accurate estimation of fundamental soil properties, such as the shear modulus ratio (G/G max) and damping ratio (D), is crucial to design and analyze various structures subjected to dynamic loads. This study presents a comprehensive investigation on harnessing the power of machine learning techniques to precisely predict G/G max and D of granular soils. Using an extensive dataset gathered from cyclic triaxial and resonant column tests on diverse mixtures of sand and gravel, combined with previous research findings, a series of advanced machine learning algorithms including shallow neural networks, support vector regression, gradient boosting regression, and deep feed forward neural network (DFFNN) were developed. The proposed models elucidate various influential parameters, including the grading characteristics, void ratio, confining pressure, consolidation stress ratio, and specimen preparation techniques. The superiority of the DFFNN model in terms of accuracy and predictive performance was demonstrated through rigorous evaluation and comparison. This study contributes to a better understanding of soil behavior under dynamic conditions. It also provides a robust framework to employ machine learning in predicting G/G max and D of granular soils, thereby enhancing the efficiency and reliability of geotechnical designs and construction practices.