桥梁退化模型及养护时机研究综述  

Review of Research on Bridge Degradation Model and Maintenance Timing

作　　者：张洪 左勤[1,2] 辛景舟 周建庭 ZHANG Hong;ZUO Qin;XIN Jing-zhou;ZHOU Jian-ting(State Key Laboratory of the Mountain Bridge and Tunnel Engineering, Chongqing 400074, China;School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China)

机构地区：[1]省部共建山区桥梁及隧道工程国家重点实验室,重庆400074 [2]重庆交通大学土木工程学院,重庆400074

出　　处：《科学技术与工程》2022年第8期2993-3001,共9页Science Technology and Engineering

基　　金：国家自然科学基金(U20A20314,51908094);重庆市杰出青年科学基金(cstc2020jcyj-jqX0006);重庆市自然科学基金创新群体科学基金(cstc2019jcyj-cxttX0004)。

摘　　要：为了准确把握桥梁结构性能退化规律,实现桥梁养护决策精准化、科学化,综述了桥梁性能退化的模型建立方法以及养护时机优化研究现状。调研了中外学者对桥梁性能退化规律和养护策略的研究成果,从物理模型、回归模型、随机模型、人工智能模型等四个方面分析了桥梁退化预测模型的研究进展,总结了桥梁养护时机优化问题的建立与解决方法。研究表明:神经网络、动态贝叶斯理论对马尔可夫模型修正可确定其最优转移概率从而使预测精度提高;高效的优化算法如布谷鸟搜索、多目标优化混合算法等可提高计算效率。研究结果可为桥梁科学养护策略提供有益参考。In order to accurately grasp the law of bridge structural performance degradation and realize the precise and scientific decision-making of bridge maintenance,the research status of model building methods of bridge performance degradation and the optimization of maintenance timing were reviewed.The research results of domestic and foreign scholars on bridge performance degradation laws and maintenance strategies were investigated,the research progress of bridge degradation prediction models from four aspects:physical models,regression models,stochastic models and artificial intelligence models were analyzed,and the optimization of bridge maintenance timing was summarized.The research shows that neural network,dynamic Bayesian theory to modify the Markov model can determine the optimal transition probability to improve the prediction accuracy.Efficient optimization algorithms such as cuckoo search,multi-objective optimization hybrid algorithms can improve calculation efficiency.Research results can provide a useful reference for scientific bridge maintenance strategies.

关 键 词：桥梁 退化预测模型 最优养护时机 优化算法 

分 类 号：U446.3[建筑科学—桥梁与隧道工程] U447[交通运输工程—道路与铁道工程]