Study On Multi-lane Transverse Reduction Factors Based On Reliability Theory And Surveying Traffic Loading Data

When conducting basic research on the basis of the probability limit state design specifications, the Ministry of Transportation has commissioned Highway Planning and Design Institute to organize forces to study vehicle load characteristics in the 1980 s and early 1990 s.But after nearly 20 years of development, the traffic has been a great change, which has a significant impact on load capacity of the vehicle, the proportion of vehicles, traffic flow characteristics of the vehicle has also been significantly changed. Whether the existing bridge design specifications can adapt to the demand of today’s traffic loads, there is a significance of exploration.Through the programing of VBA and MATLAB data processing and calculation program, the paper conducted a statistical analysis of the data based on the measured traffic load in two survey sites of Guangdong and Inner Mongolia. Then come to statistics suggested values of multi-lane transverse reduction factor under existing traffic, and put forward some suggestions to specification through comparative analysis.The main work in the following aspects:① The paper analyse traffic composition, traffic trends within the time of investigation and every lane’s daily traffic in the survey point;②Proposed a given vehicle models’ heavy vehicles probability distribution type is double Weibull distribution, through volume of traffic and the vehicle weight information. Multimodal distribution is applied to the study of multi-lane transverse reduction factor, which is one of the important research point of this article; Analysis of representative value estimate derived wheelbase data through the use of kernel density estimation; Obtaining distribution type of vehicle velocity by speed information, and take the distribution of 0.05 tantile as a representative value of velocity; Using regression analysis to fit the maximum load relationship with mean and standard deviation and analyzed changes in variation coefficient of vehicle weight’s samples; By using the probabilistic algorithms, combined with obtained calculation parameters to draw suggested values of multi-lane transverse reduction factor, then can provide a reference for norm-setting;③Using the data of the two surveys point, Transverse contrast transverse reduction factor under different circumstances to develop some suggestions for specification.