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

为提高现有沥青路面压实密度预测模型预测能力,以厦门翔安机场高速公路的路面段为试验场地,选取分别代表振压过程中谐波比、能量和力学变化的压实控制值CCV、耗散测量值DMV、振动压实值VCV以及温度作为监测指标,采用孤立森林算法进行监测指标异常点识别,基于最小偏二乘回归建立多指标沥青路面压实密度预测模型.结果表明:孤立森林算法可有效识别高维数据异常点,弥补传统方法只能处理一维数据的不足;温度与其他监测指标以及沥青路面密度存在不同程度正相关关系;基于CCV、DMV、VCV的多元回归模型拟合性能优于一元回归模型性能,论证了多指标评价方法可行性;最小偏二乘回归可改善自变量间共线性对模型权重的影响,解决温度权重与实际物理意义相互颠倒的问题;相比普通多元线性回归,最小偏二乘回归能进一步提高模型拟合能力,模型最终在训练集上的决定系数为0.83,在测试集上的决定系数为0.81,具有良好的沥青路面压实密度预测能力.

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.