大跨度斜拉桥智能可靠度评估方法研究

针对既有大跨度斜拉桥的评估管理系统中的可靠度评估问题,提出了基于RBF网络与Monte Carlo结合的可靠度评估方法。建立了招宝山大桥快速分析的RBF网络模型,网络训练样本按均匀设计方法,考虑几何非线性因素由ANSYS软件分析得到。对运营期的招宝山大桥进行了两类失效模式,三种极限状态下的可靠度评估,并分析了不同活载模式、不同功能函数及不同检测期对可靠度评估结果的影响。分析表明:基于RBF-MC的可靠度分析方法具有速度快、精度高的优点,并能同时计算多极限状态下的结构可靠指标,特别适合在基于可靠度的桥梁管理系统中采用;活载布置方式、选取的功能函数均影响可靠度评估的结果,招宝山大桥不同检测期可靠度水平变化不大,且均处于安全可靠状态。

Reliability assessment of large-span cable-stayed bridges based on artificial intelligence

A RBF neural network based Monte Carlo method is proposed to evaluate the reliability of existing large-span cable-stayed bridges for bridge assessment & management.A fast simulation RBF neural network model is established for the Zhaobaoshan bridge,and the training sample is obtained according to uniform design and using the ANSYS software for considering geometrical nonlinearities.The reliability analysis of the Zhaobaoshan bridge under two types of failure modes and three limit states and the sensitivity analysis of the live load modes,the limit state functions and the detection periods to the reliability indices are carried out.The results show that several limit states can be considered simultaneously by using the presented method.The accuracy and the efficiency of the RBF-MC method is verified from the simulation.The results of the evaluation are influenced by the live load modes and the limit state functions considered in the analysis.The Zhaobaoshan bridge is safety during the detection period.