Prediction method of pavement performance based on same dimension gray recurrence dynamic model

Abstract

In order to accurately grasp the change trend of asphalt pavement performance index, taking the rutting depth index (RDI) as an example, we establish a gray recurrence dynamic model with equivalent dimension which can use the new data effectively and dynamically. The model is used to predict the indexes such as pavement condition index (PCI), driving quality index (RQI) and skidding resistance index (SRI). The results show that in step 3, the minimum error probabilities of RDI, PCI, RQI and SRI are all 1, and the posterior prescription variance ratios are: 0.111 7, 1, 0.065 4, 1, 0.201 8, and 0.113 0, respectively. It is proved that with the increase of recursive steps, the accuracy of the prediction result of the model becomes higher, and the error becomes less, which shows that the method can accurately predict the road performance.