Optimization of bridge maintenance strategies based on structural health monitoring information
Optimization of bridge maintenance strategies based on structural health monitoring information
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DOI：
10.1016/j.strusafe.2010.05.002
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发表时间：
2011-01-01
影响因子：
5.8
通讯作者：
Frangopol, Dan M.
中科院分区：
文献类型：
--
作者：
Orcesi, Andre D.;Frangopol, Dan M.
公路桥梁会经历强度退化过程。在预算受限的情况下，确定最佳的维护策略是很重要的。基于预测模型的优化策略已经被考虑用于公路桥梁的维护和运营。预测模型通过使用无损检测方法在空间和时间上进行更新。然而，迫切需要将结构健康监测(SHM)数据有效地纳入结构评估和预测模型。事实上，安全健康管理可以控制强度退化过程，并应包括在生命周期成本模型中。结构的寿命可靠性是由残存函数表征的。SHM数据能够通过贝叶斯过程更新失效时间的概率密度函数。本文的目的有三个：(A)将健康监测数据纳入桥梁寿命周期成本分析，(B)根据监测信息确定最优维护策略，以及(C)展示健康监测的好处。考虑到没有监测结果和有监测结果的情况，确定了最佳战略；然后强调了监测的好处。建议的概念应用于美国威斯康星州威斯康星河上的1-39号北行大桥。利哈伊大学ATLSS工程研究中心对这座桥进行了监测。(C)2010爱思唯尔有限公司。保留所有权利。
Highway bridges are subjected to strength degradation processes. Under budget constraints, it is important to determine the best maintenance strategies. Optimized strategies, based on prediction models, are already considered for the maintenance and operation of highway bridges. Prediction models are updated both in space and time by using non-destructive testing methods. Nevertheless, there is an urgent need for the efficient inclusion of structural health monitoring (SHM) data in structural assessment and prediction models. Indeed, SHM allows keeping strength degradation processes under control and should be included in life-cycle cost models. The lifetime reliability of structures is characterized by survivor functions. The SHM data enable to update the probability density function of time to failure through a Bayesian process. The aim of this paper is threefold: (a) to include SHM data in a bridge life-cycle cost analysis, (b) to determine optimal maintenance strategies based on monitoring information, and (c) to show the benefits of SHM. Optimal strategies are determined considering the cases without and with including monitoring results; the benefit of monitoring is then highlighted. The proposed concepts are applied to the 1-39 Northbound Bridge over the Wisconsin River in Wisconsin, USA. A monitoring program of that bridge was performed by the ATLSS Engineering Research Center at Lehigh University. (C) 2010 Elsevier Ltd. All rights reserved.