Influencing Factors Analysis and Prediction Model of Pavement Transverse Crack Based on Big Data

Abstract

Transverse cracks dominated by reflective cracks are one of the most common diseases of expressways in our country. In order to improve the structural integrity and driving comfort, this study relied on big data stored in the PMS of Jiangsu Province to analyze the development law and influencing factors of transverse cracks in semi-rigid base asphalt pavement. Two index types, node index and development index, were proposed, and the evaluation results and significant influencing factors of these two evaluations indexes were analyzed respectively. The fitting model function in JMP software was used to statistically analyze the traffic and structural influence factors of the transverse crack in the total mileage of 854 km of 291 original road sections. Then the SCB test was carried out to obtain the fracture energy of each sublayer of the sections. At last, a TCS prediction model was constructed by the significant influencing factors and the composite fracture energy representing the overall crack resistance level of the asphalt layer. The results of this study show that 85% of the road sections cracked for the first time within 3–9 years of opening to traffic. The first 5 years after opening to traffic was the slow development stage of transverse cracks, and the 5–10 years was the stage of rapid development of transverse cracks. 10–15 years later, the development speed of transverse cracks tended to slow down. The main factors affecting the generation and development of transverse cracks are traffic volume, the gradation type of each layer, the thickness of modified asphalt layer and the type of base material.