Asphalt Pavement Longitudinal Sliding Performance Testing And Evaluation Study

The pavement skid resistance is one of important indicators in performance evaluation for road safety, and it includes longitudinal skid resistance and transversal skid resistance. A lot of basic works have been done in the research and application of asphalt pavement skid resistance in recent years, but there are still many problems on asphalt pavement skid resistance measuring and evaluating in our country. Firstly, the study of asphalt pavement longitudinal skid resistance is weak; secondly, the criterion in evaluating the pavement skid resistance is not unified; thirdly, the limitations of the existing criterias for evaluating the pavement skid resistance.For the above problems, this pape starts as the mechanism of skid resistance analysis and impact factors and does an intensive study of the friction causes, friction type and friction factors between tire and road surface, a detailed introduction has been made for asphalt pavement longitudinal skid resistance measurement equipment, which is called GripTester, and a experimental study has been made applying GripTester for the impact factors of the asphalt pavement longitudinal skid resistance, the influencing characteristic of impact factors in different conditions has been discussed, firstly proposes the method to apply the BP artificial neural network for asphalt pavement longitudinal skid resistance evaluation,and this method uses pavement texture parameters as evaluation index, it is the first exploratory research to make a comprehensively and objectively evaluation for pavement skid resistance.The main conclusions of this paper are as follows:(1)The testing data of GripTester with good stability and reliability, GripTester can be used to measure asphalt pavement longitudinal friction coefficient rapidly and continuously;(2)There is a good correlation between traffic speed and asphalt pavement longitudinal skid resistance, the longitudinal friction coefficient decreased with increase of traffic speed, the decrease rate in 50～90km/h is bigger than in 10～50km/h speed interval, the impact characteristics of the traffic speed of a great influence on macro-structural conditions, the smaller the value of the macro-structure, the greater decline rate of the friction coefficient;(3)Asphalt pavement textures have a significant impact on its longitudinal skid resistance, the longitudinal friction coefficient increasesd with increase of the value of the micro-structure, macro-structure, but the impact degree of the micro-structure decreased with increase of the taffic speed, the impact degree of the macro-structure increased with increase of the taffic speed;(4)The water film thickness of asphalt pavement has a significant impact on asphalt pavement longitudinal skid resistance, the longitudinal friction coefficient decreased with increase of the water film thickness, and the impact characteristics of the water film thickness of a great influence on macro-structural conditions, the smaller the value of the macro-structure, the greater decline rate of the friction coefficient; (5)The asphalt pavement longitudinal skid resistance is a product of a system,so in order to evaluate the longitudinal skid resistance comprehensively and objectively,it is must to attribute from pavement textures parameters and take into account traffic speed and the water film thickness of asphalt pavement;(6)The micro-texture calculation model and skid resistance evaluation model has been built with using BP neural network method, and the performances of the models are good by test data proof. The method for evaluating asphalt pavement longitudinal skid resistance suggested in this paper uses pavement texture parameters as evaluation index, and working through evaluation models, it is the first exploratory research to make a comprehensively and objectively evaluation for pavement skid resistance.A systematic study on measurement and evaluation for asphalt pavement longitudinal skid resistance has been done in this paper, the method based on BP neural network model for evaluating the skid resistance is first proposed, and it is of important engineering reference value.