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Longitudinal skid resistance of asphalt pavement testing and evaluation of technical

Author: HuYaNan
Tutor: ZhangZuo；ZhongYanHui
School: Zhengzhou University
Course: Road and Railway Engineering
Keywords: Asphalt Pavement Longitudinal skid resistance GripTester Pavement structure BP neural network
CLC: U416.217
Type: Master's thesis
Year: 2009
Downloads: 92
Quote: 0
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Abstract


Pavement skid resistance is an important indicator of road safety performance evaluation, which includes longitudinal skid resistance and two lateral skid resistance. In recent years, a lot of basic work to do in the asphalt pavement skid research and application, but there are still many deficiencies, weak longitudinal skid resistance of asphalt pavement; skid resistance evaluation standards are not uniform; Furthermore, there are some limitations skid resistance evaluation criteria. Problems currently exist in the longitudinal skid resistance of asphalt pavement research from the asphalt pavement skid resistance mechanism and influencing factors of analyzing, in-depth study of the causes of friction between the tire and the road, the friction type as well as the influence of friction factors, described in detail road longitudinal skid resistance the testing equipment GripTester and the application GripTester asphalt pavement longitudinal skid resistance were studied, longitudinal skid resistance of the asphalt pavement under the various factors affecting the variation, and the first time application of BP artificial neural network model to evaluate the longitudinal skid resistance of asphalt pavement, asphalt pavement structure itself attributes to comprehensively and objectively reflect the longitudinal skid resistance, is a new attempt and exploration. The main conclusions are as follows: (1) GripTester test data has good stability and reliability, can be used to achieve the asphalt pavement longitudinal friction coefficient rapid and continuous detection; (2) the driving speed and asphalt pavement longitudinal skid resistance good correlation, the longitudinal friction coefficient increases as the driving speed is reduced, the rate of decrease of the friction coefficient is greater than 10 to 50 km / h speed range interval length in 50 to 90km / h speed and driving speed affect the characteristics of the road surface macro-structure conditions affecting a large, macroscopic structure the smaller the value of the road surface, the longitudinal road friction coefficient decreases with increasing speed, the greater the rate; (3) asphalt pavement construction parameter has a significant impact on the longitudinal skid performance, the longitudinal friction coefficient with the micro- configured value, the macro-configured value increases, but the degree of micro-structural impact decreases with increasing speed, and macroscopic structure affect the extent becomes larger as the speed increases; (4) asphalt pavement with a water film thickness on the asphalt The longitudinal skid resistance of longitudinal friction coefficient decreases with the increase of the thickness of the water film, and the water film thickness of the characteristics are affected by macro-structural conditions of the road, pavement macro-structure the smaller the value, the increase of the longitudinal coefficient of friction with the water film thickness reduced rate increases; (5) asphalt pavement longitudinal skid resistance is the product of a system, only the departure from the road attribute parameters, taking into account the combined effects of vehicle speed and water film thickness, to comprehensive, objective pavement longitudinal skid resistance make evaluations; (6) In this paper, BP neural network created by the micro-structure calculation model and skid resistance evaluation model of good performance, road pavement structure parameters as indicators model is comprehensive and objective evaluation of anti- slip performance for the first time to explore. Longitudinal asphalt pavement skid resistance testing and evaluation system, and proposed evaluation method based on BP neural network sliding performance model, with a important engineering reference value.

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CLC: > Transportation > Road transport > Road works > Roadbed, pavement engineering > Pavement Engineering > Road : use of the material points > Asphalt Pavement
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