期刊
STRUCTURAL SAFETY
卷 33, 期 1, 页码 26-41出版社
ELSEVIER
DOI: 10.1016/j.strusafe.2010.05.002
关键词
Structural health monitoring; Maintenance; Survivor functions; Cost; Bayesian updating; Optimization
资金
- National Science Foundation [CMS-0639428]
- Commonwealth of Pennsylvania, Department of Community and Economic Development, through the Pennsylvania Infrastructure Technology Alliance (PITA)
- US Federal Highway Administration [DTFH61-07-H-00040]
- US Office of Naval Research [N-00014-08-0188]
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.
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