基于灰色关联度分析和支持向量机 回归的沥青路面使用性能预测

沥青路面使用性能多因素预测是一个复杂的非线性问题,传统预测模型存在很多不足。为弥补传统模型的缺陷,建立一个高精度、长周期、多因素的预测模型,通过灰色关联度分析对各因素进行降维处理,选择与沥青路面使用性能关联度较大的影响因素进行支持向量机回归非线性预测,提出了基于灰色关联度分析和支持向量机回归(GRA-SVR)的沥青路面使用性能预测模型。最后选用广云高速实测车辙指数(RDI)值进行实例验证,并同GM(1,1)和PPI两种模型的预测结果进行了对比分析。结果表明:基于GRA-SVR建立的多因素预测模型具有很好的精度和可操作性,可在长周期过程中使用,为大数据养护决策提供了模型参考和依据。

Performance prediction of asphalt pavement based on grey relational analysis and support vector machine regression

Asphalt pavement performance prediction is complex and nonlinear when it involves multi-factor. In order to overcome the defects existing in traditional prediction models, a long-period and multi-factor prediction model with high precision needs to be established, on which the dimension of each factor is reduced by grey relational analysis, and the important relational factors are selected for nonlinear prediction by support vector machine regression. Accordingly the performance prediction model of asphalt pavement based on GRA-SVR was proposed and the measured RDI from Guangyun freeway were collected as an example to validate the proposed model. The results show that GRA-SVR model has better accuracy and maneuverability compared with GM(1,1)and PPI models. It can be used in long-term process and provide model reference for large data maintenance decision-making.