江西建材Issue(12)：292-295,4.
土壤压实参数智能预测方法研究
Research on Intelligent Prediction Method of Soil Compaction Parameters
摘要
Abstract
Maximum dry density and optimal moisture content are two important parameters of subgrade compaction,which affect the stability and bearing capacity of subgrade,and thus affect the stability and durability of infrastructure such as highways and Bridges.Therefore,it is very important to effectively predict the maximum dry density and the best moisture content.Pearson correlation analysis was used in this pa-per.The correlation between plastic limit and maximum dry density and optimal water content was the highest,followed by liquid limit and fine particle content,while specific gravity had no correlation with maximum dry density and optimal water content.Therefore,in this paper,three machine learning algorithms,Random Forest(RF),support vector machine(SVM)and K-nearest neighbor algorithm,were used to establish a prediction model with plastic limit,liquid limit and fine particle content as inputs and maximum dry density and optimal moisture content as outputs.After training and testing,the best models of the three algorithms are compared in this paper.The results show that random forest model(RF)is the best model to predict the maximum dry density and the best water content.The correlation coefficient R2 is 0.894 5,the mean absolute error(MAE)is 0.162 5,the R2 of the best water content is 0.886 5,the mean absolute error(MAE)is 0.001 2.Using ma-chine learning model to predict soil compaction parameters can improve the efficiency of engineering construction and has certain guiding signif-icance for construction.关键词
土壤压实参数/最大干密度/最佳含水率/机器学习Key words
Soil compaction parameters/Maximum dry density/Optimum moisture content/Machine learning分类
交通工程引用本文复制引用
唐亮,张晓飞,李鸿钊,张文俊,郑军涛,刘广波..土壤压实参数智能预测方法研究[J].江西建材,2024,(12):292-295,4.基金项目
山东省交通运输科技项目《高速公路路基韧性提升与智能压实综合评价技术研究》(项目编号:2023B45). （项目编号:2023B45）
