Random vehicle flow load effect considering axle load

被引：5
|
作者：
Li M. [1 ]
Liu Y. [1 ]
Yang X.-S. [2 ]
机构：
[1] Hunan Province Research Center for Safety Control Technology and Equipment of Bridge Engineering, Changsha University of Science and Technology, Changsha
[2] Sichuan Vocational and Technical College of Communications, Chengdu
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2019年 / 53卷 / 01期
关键词：
Axle correlation; Bridge engineering; Copula; Load effect; Random vehicle flow;
D O I：
10.3785/j.issn.1008-973X.2019.01.009
中图分类号：
学科分类号：
摘要：
The random vehicle flow parameters especially axle load correlation were analyzed based on the measured data of YI LU highway WIM in order to provide reference for the design load of bridge checking computations. A random vehicle flow simulation program was worked out by MATLAB. The load effect of random vehicle flow was analyzed by the influence line method, and the difference of load effect between considering the axle load correlation and without considering was discussed. Results show that axle load has the characteristics of multimodal distribution. There is a strong correlation between axle loads. Greater error will be caused when simulating random vehicle flow without considering the axle load correlation. The joint distribution function of vehicle axle load can accurately describe the joint distribution of vehicle axle load by using the nonparametric kernel density estimation-Copula method and using the t-Copula function as the connection function. The load effect considering the axle load correlation is 45% bigger than without considering, and it is closer to the representative value of measured traffic load effect. There is no significant difference between representative values of the load effect of the each lane random vehicle flow on the simply-supported beam bridge. Representative values of the load effect have exceeded the load effect of the current specification highway level I lane load, and the maximum effect ratio is 1.34. © 2019, Zhejiang University Press. All right reserved.
引用
收藏
页码：78 / 88
页数：10
相关论文
共 13 条
  • [1] Mayrai G., Development of a reliability-based deflection limit state for steel girder bridges, (2004)
  • [2] Castillo E., Menendez J.M., Sanchez-Cambronero S., Et al., A hierarchical optimization problem: estimating traffic flow using Gamma random variables in a Bayesian context, Computers and Operations Research, 41, pp. 240-251, (2014)
  • [3] Jabari S.E., Liu H.X., A stochastic model of traffic flow: theoretical foundations, Transportation Research Part B: Methodological, 46, 1, pp. 156-174, (2012)
  • [4] Yu Z.-W., Zhu H.-B., Jiang L.-Z., Et al., Vehicles load stochastic process model of highway bridges, Journal of Central South University: Science and Technology, 42, 10, pp. 3131-3135, (2011)
  • [5] Sun S.-W., Sun L.-M., Statistic model of vehicle loads for highway bridges, Journal of TongJi University: Science and Technology, 40, 2, pp. 198-204, (2012)
  • [6] Guo T., Li A.-Q., Zhao D.-L., Multiple peaked probability vehicle load model for highway bridge reliability assessment, Journal of Southeast University: Natural Science Edition, 38, 5, pp. 763-766, (2008)
  • [7] Zong Z.-H., Li F.-F., Xia Y.-F., Et al., Study of vehicle load models for Xinyi river bridge based on WIM data, Bridge Construction, 43, 5, pp. 29-36, (2013)
  • [8] Nowak A.S., Nassif H., Live load models based on WIM data, Probabilistic Mechanics and Structural and Geotechnical Reliability, (2015)
  • [9] Mei G., Qin Q., Lin D.-J., Bi-model probabilistic model of highway and bridge vehicle loads, Journal of Tsinghua University: Science and Technology, 43, 10, pp. 1394-1396, (2003)
  • [10] Sklar A., Fonctions de Repartition n Dimensions et Leurs Marges, 8, pp. 229-231, (1959)
12