Pavement Rut Depth Prediction for a Three-Span Suspension Steel Box Girder Bridge Based on Two-Year Temperature Monitoring Data

Abstract

The paper presents a study of temperature distribution and effect on the asphalt pavement of the Fourth Nanjing Yangtze River Bridge based on the 2-year continuous monitoring data. The temperature distribution model was incorporated into a simplified rut-depth prediction equation and a finite-element analysis (FEA) model. The prediction results based on the two methods were compared with the testing results. First, a generalized extreme value distribution function was chosen to describe the probability distribution of the pavement temperatures. With properly selected fitting parameters, it was found that the generalized extreme value distribution function fit the monitoring temperature data well. Then, an empirical rut-depth prediction formula was adopted considering effects of asphalt dynamic modulus, dynamic stability, temperature influence, vehicle speed and location, and annual average daily traffic. Thanks to the development of the pavement temperature distribution model, the sum of time duration for temperature above 40°C could be calculated and the temperature coefficient in the simplified rutting prediction equation could be obtained. On the other hand, the temperature duration was also incorporated into the three-dimensional (3D) FEA model based on the modified Burger’s model, accounting for pavement’s viscoelasticity, to make the rutting evaluation using FEA possible. It indicated that the simplified rutting prediction yielded a better result than the comprehensive FEA result, which suggests a fast and simple method for local practical engineers. It was suggested that more efforts be made to improve the accuracy of the FEA method in the future.