基于数据驱动的机场水泥混凝土道面性能退化预测

针对机场道面性能退化过程的精确预测问题，本文采用数据驱动的机场道面预测性维护方法，通过指示变量将两种数据集进行联合分析，考虑机场道面性能退化过程受飞行交通量和道面面层厚度的影响，以机场道面性能状况指数衰变的非线性函数为期望函数，建立一种机场道面性能退化双参数预测模型；根据模型的参数估计结果，采用边际效应分析结合预测性能曲线图示，对不同飞行交通量水平和不同道面厚度等级的道面性能退化过程预测进行分析。结果表明，采用数据驱动和非线性混合效应方法，搭载联合估计技术，能较为显著地提高机场道面性能退化预测的精度和效果。

Predicting Performance Degradation for Airport Portland Cement Concrete Pavements Based on Data-driven

In order to accurately predict the airport pavement performance degradation, this paper adopts a data-driven predictive maintenance method, analyzes two data sets by means of indicator variables, considers the influence of flight traffic volume and pavement surface thickness, and establishes a two-parameter prediction model for airport pavement performance degradation with taking the nonlinear function of decay of airport PCI(pavement condition index) as the expectation function. Based on the parameter estimation results of the model, a marginal effect analysis combined with the predicted performance curve is used to analyze the prediction of the degradation under different flight traffic levels and different pavement surface thickness classes. The results show that the data-driven method and nonlinear mixedeffects approach with the joint estimation technique can significantly improve the accuracy and effectiveness of airport pavement performance degradation prediction.