钢结构中疲劳裂纹增长概率模型和Bayesian动态预测  

Bayesian Dynamic Prediction and Probabilistic Model of Fatigue Crack Growth in Steel Structures

作　　者：陈梦成[1,2] 方苇[1,2] 杨超[1,2] 谢力[1,2] CHEN Mengcheng;FANG Wei;YANG Chao;XIE Li(State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure,East China Jiaotong University,Nanchang 330013,China;School of Civil Engineering and Architecture,East China Jiaotong University,Nanchang 330013,China)

机构地区：[1]华东交通大学省部共建轨道交通基础设施性能监测与保障国家重点实验室,江西南昌330013 [2]华东交通大学土木建筑学院,江西南昌330013

出　　处：《铁道学报》2021年第7期177-184,共8页Journal of the China Railway Society

基　　金：国家自然科学基金(51878275,51378206);江西省教育厅科学技术研究(GJJ190299)。

摘　　要：疲劳裂纹在桥梁结构中非常普遍,其增长过程是一个随机过程;另外,疲劳裂纹增长会引起桥梁结构性能退化。对桥梁结构性能退化的预测应基于桥梁的现状,结合具体构件的实际监测数据进行更新。首先依据线弹性断裂力学(LEFM)理论和Paris半经验疲劳裂纹扩展速率公式,建立钢结构的疲劳裂纹增长概率模型;然后利用Bayesian更新理论和马尔卡夫链蒙特卡罗模拟,结合试验数据,构建疲劳裂纹增长概率模型随机参数的更新方法,进而实时预测钢构件性能退化轨迹和寿命评估。使用提出的方法结合已有的试验数据进行了模拟仿真试验,结果表明,方法可以实现疲劳裂纹增长模型中随机参数的更新,有效地预测钢结构性能退化轨迹和时变可靠性的变化规律。Fatigue cracks are very common in bridges,whose propagation is a random process.In addition,the propagation will deteriorate the bridge structural performances.The prediction of bridge structural performance deterioration should be updated based on the current situation of the bridge,and the actual monitoring data of specific components.In this paper,a probabilistic model of fatigue crack growth in steel structures under fatigue loading was first proposed based on the linear elastic fracture mechanics(LEFM)and the Paris semi-empirical formulation for fatigue crack growth.Then the Bayesian updating method and Markov Chain Monte Carlo(MCMC)simulation were employed to build the update method for the random parameters of the fatigue crack growth model based on test data,to conduct real-time prediction of the structural life and the trajectory of structural performance degradation.A simulation test was conducted using the proposed method in combination of the existing test data.The results indicate that the method can be used to update the random parameters in the fatigue crack growth model,and can effectively predict the variation of steel structural performance degradation trajectory and structural time-dependent reliability.

关 键 词：桥梁工程 钢结构 Bayesian更新 疲劳裂纹 概率模型 时变可靠性 

分 类 号：U447[建筑科学—桥梁与隧道工程] O213.2[交通运输工程—道路与铁道工程]