Characteristics of physical parameters and predictive modeling of mechanical properties in loess-like silty clay for engineering geology

In the middle and lower reaches of the Yellow River in China, loess-like silty clay is prevalent. This soil type exhibits considerable variability in its compression coefficient alpha, which can lead to differential soil settlement and consequent damage to buildings and infrastructure, thereby posing safety risks. Despite its significance, research and data on this topic are still limited. This study involves comprehensive measurement and laboratory analysis of over one thousand soil samples collected on-site. It establishes a statistical distribution model for essential parameters, including water content w, wet density rho, void ratio e, saturation Sr, liquidity index IL, liquid limit WL, plastic limit WP, and plasticity index IP, and explores the probability distribution characteristics of the physical and mechanical parameters of loess-like silty clay. Machine learning prediction models, utilizing Random Forest (RF) and Deep Neural Network (DNN) algorithms, were developed based on an extensive database to forecast the compression coefficient alpha and compression modulus ES of this soil. The predictive models demonstrated higher accuracy compared to conventional methods and hold significant practical implications for the timely prediction of the mechanical and engineering characteristics of loess-like silty clay. This research provides a robust scientific foundation for engineering design, enhances understanding of the mechanical properties and engineering attributes of this special soil expanse, and reduces the high costs and time consumption associated with engineering geological surveys, as well as the subjectivity of technical and environmental constraints and data interpretation. It serves as a valuable tool for disaster prevention and prediction.