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 , 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 , wet density , void ratio , saturation , liquidity index , liquid limit , plastic limit , and plasticity index , 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 and compression modulus 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.

中文翻译：

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工程地质类黄土粉质粘土物理参数特征及力学性能预测模型


我国黄河中下游地区盛行黄土状粉质粘土。这种土壤类型的压缩系数表现出相当大的变化，这可能导致土壤沉降差异，从而对建筑物和基础设施造成损害，从而带来安全风险。尽管其意义重大，但有关该主题的研究和数据仍然有限。这项研究涉及对现场采集的一千多个土壤样本进行综合测量和实验室分析。建立了含水量、湿密度、空隙率、饱和度、流动性指数、液限、塑限、塑性指数等基本参数的统计分布模型，探讨了黄土物理力学参数的概率分布特征。像粉质粘土。基于广泛的数据库，利用随机森林 (RF) 和深度神经网络 (DNN) 算法开发了机器学习预测模型，以预测该土壤的压缩系数和压缩模量。与传统方法相比，预测模型具有更高的准确性，对于及时预测类黄土粉质粘土的力学和工程特性具有重要的实际意义。该研究为工程设计提供了坚实的科学基础，增强了对这种特殊土壤的力学特性和工程属性的理解，并减少了与工程地质调查相关的高成本和时间消耗，以及技术和环境约束的主观性和数据解释。它是灾害预防和预测的重要工具。