基于贝叶斯更新和逆高斯过程的在役钢筋混凝土桥梁构件可靠度动态预测方法

在役钢筋混凝土桥梁在服役环境和车辆荷载的耦合作用下，其服役性能随时间不断退化，采用确定性的性能退化模型无法准确描述退化过程中的不确定性和时间变异性。该文采用逆高斯随机过程描述其抗力退化过程，同时采用复合泊松过程描述车辆荷载效应，建立了基于抗力-车辆荷载效应双随机过程的在役钢筋混凝土桥梁构件时变可靠度分析方法。结合检测数据，采用贝叶斯分析和期望最大化算法，对逆高斯过程抗力退化模型参数进行更新，提出了在役钢筋混凝土桥梁构件可靠度的动态预测方法。以一座钢筋混凝土T梁桥为例，采用其40年服役期的抗力退化数据，分别在四个服役时刻对逆高斯过程抗力退化模型参数进行了更新，演示了提出的可靠度动态预测方法。研究表明：逆高斯随机过程可更合理地描述钢筋混凝土桥梁构件抗力退化过程中的不确定性和时间变异性；融入桥梁服役期间检测的抗力退化信息，采用贝叶斯更新逆高斯过程抗力退化模型参数后，可更准确地预测桥梁未来的可靠度服役状况和估计桥梁的剩余使用寿命。

DYNAMIC PREDICTION OF RELIABILITY OF IN-SERVICE RC BRIDGES USING THE BAYESIAN UPDATING AND INVERSE GAUSSIAN PROCESS

The performance of reinforced concrete(RC)bridges deteriorates with time under the combined action of environmental effects and loadings.The deterministic degradation models cannot accurately describe the uncertainty and time variability of the degradation process.In this study,the inverse Gaussian process(IGP)and the composite Poisson process were respectively adopted to describe the resistance degradation process and the load effect.The time-dependent reliability analysis method for RC bridge members was established based on the double dual random process model of resistance and load.Furthermore,combined with the monitored data,the Bayesian analysis and the expectation maximization(EM)algorithm were adopted to update the IGP-based resistance deterioration model parameters.Subsequently,a dynamic prediction procedure for the reliability of RC bridge members was proposed.Finally,using the resistance degradation data of a RC T-girder bridge during its 40-year service period,the IGP-based resistance deterioration model parameters were updated at four different service times.The reliability of the proposed dynamic prediction procedure was validated.The research results show that IGP can be used to describe the uncertainty and time variability of the resistance degradation process of RC bridge members.The future reliability level and the remaining service life of RC bridges can be accurately predicted by using the detected resistance degradation data during its service period and the updated IGP-based resistance deterioration model parameters.