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.

This study, using Jinan as a case study, systematically investigates the characteristics and geological genesis of loess-like silty clay in the middle and lower reaches of the Yellow River. The primary distribution of loess-like silty clay is revealed through field surveys, laboratory experiments, and previous literature reviews. The chemical and physical properties of the loess-like silty clay were examined, in addition to investigations into its mineral composition, microstructural characteristics, and engineering mechanical properties, in order to enhance comprehension of its attributes and formation mechanisms. The research suggests that the distinctive soil environment in the area has been influenced by numerous instances of the Yellow River overflow and channel shifts over its history, as well as the impacts of climate change, geological factors, and human activities. The primary sources of material for the loess-like silty clay consist of loess, Hipparion Red Clay, and paleosol layers. The discussion also addresses the impact of regional climate on the formation of mineral components. The aforementioned findings hold significant implications for advancing the understanding of historical climatic and paleogeographic shifts, as well as for addressing engineering challenges associated with the distribution of loess-like silty clay.