Estimation of Extreme Value Vehicle Load Based on the Extended Burr XII Distribution

Structural Engineering
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Abstract

Traffic monitoring, and particularly maximum vehicle load, is a very important for predicting the remaining service time of either long span or short-to-medium span bridges. Using weigh in motion (WIM) data from the Nanxi Yangtze river bridge, a novel maximum load estimation model of vehicle load was constructed. The novel model is based on the extended Burr XII (EBurr) distribution, which includes the Weibull, generalized Pareto (GPD) and log-logistic distributions. Thus, the traditional GPD model is a special form of the proposed novel model. The correlation of vehicle load is extracted using a peak over threshold method, and a Markov chain Monte Carlo Bayesian method is applied to estimate the parameters. The proposed novel model is compared with other traditional models. The 95th percentile of the load distribution is considered as the evaluation point for overloaded trucks. In addition, vehicle loads collected on highway station are used to verify the novel model’s applicability. The results show: The EBurr distribution is more suitable to capture sparse extreme points than other traditional distributions according to the value of SSE closely to 0 and R2 closely to 1. When the assessment reference period T changes from 100 to 30 years, the deceased ration of the evaluation load weight is 15.17% of EBurr and 10% of GPD of the Nanxi Yangtze river bridge, where it is 12.17% of EBurr and 0.84% of GPD of the bridge near La linhe highway station. The deceased ration of the evaluation load weight using EBurr is larger than that using GPD. Moreover, the deceased ration of the evaluation load weight using GPD in La linhe highway station has a little change. Hence, using EBurr distribution to model the evaluation load is more correspond to fact.

Keywords

long span bridge vehicle load extreme value distribution the Extended Burr XII distribution Bayesian estimation Markov chain Monte Carlo simulation 
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Copyright information

© Korean Society of Civil Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Civil Engineering and ArchitectureChangsha University of Science and TechnologyChangshaChina
  2. 2.School of Mathematics and StatisticsChangsha University of Science and TechnologyChangshaChina

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