湖南大学学报（自然科学版）2024，Vol.51Issue(11)：147-157,11.DOI:10.16339/j.cnki.hdxbzkb.2024116
沥青路面压实密度多指标智能预测模型
Intelligent Compaction Density Prediction Model of Asphalt Pavement Based on Multiple Indicators
摘要
Abstract
To improve the predictive ability of existing models for predicting the compaction density of asphalt pavement,a test site was set up on the upper layer of the Xiang'an Airport Highway Project in Xiamen.The CCV,DMV and VCV,which represent the change of harmonic ratio,energy,and mechanics in the vibration and compression process,respectively,as well as the temperature,were chosen as indicators.The isolation forest algorithm was used to detect outliers in indicators.The density prediction model was established based on the partial least squares regression.The results show that the isolation forest can effectively recognize outliers of high-dimensional data,covering the shortage that traditional methods can only process one-dimensional data.There are different degrees of positive correlation between temperature,other indicators,and asphalt pavement density.The multiple regression model based on CCV,DMV,and VCV obtains better fitting ability than the unitary regression methods,proving the feasibility of multiple indicators.The partial least squares regression can restrain the adverse impact caused by the approximate collinearity between independent variables,correct the incorrect weight of temperature,and improve the fitting degree compared with the common multiple linear regression methods.The final determination coefficient of the model on the training set is 0.83,and on the test set is 0.81,indicating good predictive ability for asphalt pavement density.关键词
沥青路面/智能压实/智能压实测量值/孤立森林/最小偏二乘回归Key words
asphalt pavement/intelligent compaction measurement values/index of continuous compaction monitoring/isolation forest/partial least squares regression分类
交通工程引用本文复制引用
阙云,戴伊,薛斌,章灿林,牟宏霖,袁燕..沥青路面压实密度多指标智能预测模型[J].湖南大学学报（自然科学版）,2024,51(11):147-157,11.基金项目
国家自然科学基金资助项目(52008113),National Natural Science Foundation of China(52008113) （52008113）
