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基于年均小时交通量的卡车荷载预测模型及其对桥梁疲劳损伤影响
引用本文:刘浪,杨洪,叶仲韬.基于年均小时交通量的卡车荷载预测模型及其对桥梁疲劳损伤影响[J].重庆大学学报(自然科学版),2021,44(10):117-129.
作者姓名:刘浪  杨洪  叶仲韬
作者单位:重庆交通大学 省部共建山区桥梁及隧道工程国家重点实验室,重庆 400074;重庆交通大学 土木工程学院,重庆 400074;重庆交通大学 土木工程学院,重庆 400074;中铁大桥科学研究院有限公司,武汉 430034;桥梁结构健康与安全国家重点实验室,武汉430034
基金项目:国家自然科学基金资助项目(51708069);重庆市基础研究与前沿探索项目(cstc2018jcyjA2535)。
摘    要:车辆实测数据表明,车辆荷载存在明显递增趋势.利用中国安徽省某桥梁长期健康监测数据,定义年平均小时交通量(AAHT)来考虑交通量的周期性和季节性变化,并据此建立季节性差分自回归移动平均模型(SARIMA)模型对未来交通荷载进行预测.同时,基于实测卡车数据的关键参数统计结果,建立多种类型的卡车荷载模型,逐一加载到某T梁桥有限元模型上,研究卡车荷载的非平稳增长对结构疲劳损伤的影响.结果表明,基于AAHT的季节性差分自回归移动平均模型能够准确预测车辆荷载,且车辆的非平稳增长对桥梁疲劳损伤影响显著,考虑车辆荷载非平稳增长时桥梁的疲劳损伤度约为不考虑车辆非平稳增长时的1.7倍.

关 键 词:年平均每小时交通量  季节性差分自回归滑动平均模型  车辆荷载预测  疲劳损伤
收稿时间:2020/3/31 0:00:00

AAHT-based truck load simulation model and its impacts on bridge fatigue damage
LIU Lang,YANG Hong,YE Zhongtao.AAHT-based truck load simulation model and its impacts on bridge fatigue damage[J].Journal of Chongqing University(Natural Science Edition),2021,44(10):117-129.
Authors:LIU Lang  YANG Hong  YE Zhongtao
Institution:State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, P. R. China;School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, P. R. China; China Railway Bridge Science Research Institute Ltd., Wuhan 430034, P. R. China;State Key Laboratory for Health and Safety of Bridge Structures, Wuhan 430034, P. R. China
Abstract:The recorded traffic data show that traffic loads have been increasing. In this study, based on the long-term health monitoring data collected from Anhui Province, the annual average hourly traffic (AAHT) is defined with taking into account the periodical and seasonal change of traffic volumes, and furthermore, an autoregressive moving average model (SARIMA) is established to simulate truck loads in the future. At the same time, several truck-load models are developed with the statistics of the key parameters of truck data, and then loaded one by one on the finite element model of a T-bridge to calculate the fatigue damage induced by the non-stationary increases of truck traffic. The results show that the AAHT-based SARIMA model is accurate and efficient for predicting traffic loads, and the non-stationary increase of traffic loads will significantly jeopardize bridge structures. The fatigue damage with considering the non-stationary increase is about 1.7 times of the fatigue damage without considering the non-stationary increase.
Keywords:AAHT  SARIMA model  traffic load simulation  fatigue damage
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