Developing a skid resistance prediction model for newly built pavement: application to a case study of steel bridge deck pavement
Yang Liu, Zhendong Qian, Haibo Hu, Xijun Shi, Leilei Chen
Abstract
Skid resistance is vital to road safety. The skid resistance of steel bridge deck pavement (SBDP) is particularly important due to the special operating conditions and unique structure features for bridges. This study aims to propose an effective method to predict the skid resistance of newly built SBDP at the design stage. First, the experiment was designed by Taguchi method to get the skid resistance-related data. Secondly, sensitivity analysis was conducted to evaluate the influence of material design and construction factors on the skid resistance. Finally, a prediction model based on GA-BP neural network (back propagation network optimised by genetic algorithm) was developed. Results show that the mixture design parameters have stronger influences on the skid resistance than the construction factors of SBDP. From this study, the 9-14-1 (Input neurons – Hidden neurons – Output neuron) GA-BP model with the population size of 60 could yield an acceptable prediction result for the skid resistance.