基于实测荷载的铁路钢桁梁桥疲劳可靠性评估

为了对钢桁梁桥疲劳进行评估,引入可靠性理论,提出了基于实测荷载的桥梁疲劳可靠性评估方法。根据某铁路钢桁梁桥实测车辆荷载数据,建立了随机车辆荷载模型。并在考虑车辆荷载随机性的基础上,结合Monte-Carlo法与有限元,分析了钢桁梁桥构件疲劳应力谱,计算了构件疲劳可靠性随时间的变化,并探讨了车辆荷载及荷载效应变异性对构件疲劳可靠性的影响,最后采用β约界法与静力分析法对钢桁梁桥系统疲劳可靠性进行了研究。结果表明,基于随机车辆荷载的构件疲劳应力谱呈现单峰分布;构件疲劳可靠性随运营时间的增加而减小;车辆荷载的增长及荷载效应变异性的增加对构件疲劳可靠性影响较大,当车辆荷载增长率和等效应力变异系数分别增加到5%时,构件疲劳寿命大幅减小;β约界法结合静力分析法可快速确定钢桁梁桥失效模式,桥梁系统疲劳寿命小于构件疲劳寿命。总的来看,基于实测荷载的钢桁梁桥疲劳可靠性评估方法能有效地利用监测数据,对桥梁疲劳评估具有良好的适用性。

Fatigue reliability evaluation of railway steel truss bridge based on measured load

In order to evaluate the fatigue of a steel truss bridge,a reliability theory is introduced,and a fatigue reliability assessment method based on measured load data is proposed.According to the measured load data of a railway steel truss bridge,a random vehicle load model is established.On the basis of the randomness of vehicle loads,the fatigue stress spectrum of members of the steel truss bridge is analyzed by combining Monte-Carlo method with the finite element method,the variation of fatigue reliability with time is calculated,and the influence of variability of vehicle loads and load effects on the fatigue reliability of members is discussed.Finally,the fatigue reliability of the steel truss bridge system is studied by theβ-bound method and static analysis method.The results show that the fatigue stress spectrum of the members based on the random vehicle load shows a single peak distribution.The increase of the vehicle load and variability of load effect have the great influence on fatigue reliability of components,when the growth rate of the vehicle load and the variation coefficient of the equivalent stress increase to 5%,the fatigue life of the component is greatly reduced.Theβ-boundary method combined with the static analysis method can quickly determine the failure mode of the steel truss bridge,and the fatigue life of the system is less than the fatigue life of the members.In general,the steel truss bridge fatigue reliability assessment method based on the measured load can effectively use the monitoring data and has good applicability.