Abstract
Bridge inspection standards in the United States require routine visual inspections to be conducted on most bridges at a maximum interval of two years regardless of the bridge condition. Limitations of this uniform calendar-based approach have been reported in the literature. Accordingly, the objective of this study is to provide a new systematic approach for inspection planning that integrates information from bridge condition prediction models, inspection data, and expert opinion using Bayesian analysis to enhance inspection efficiency and maintenance activities. The uncertainty-based inspection framework proposed in this study can help bridge owners avoid unnecessary or delayed inspections and repair actions, determine the inspection method, and consider more than one deterioration process or bridge component during the inspection planning process. The inspection time and method are determined based on the uncertainty and risks associated with the bridge condition. As uncertainty in the bridge condition reaches a defined threshold, an inspection is scheduled utilizing nondestructive techniques to reduce the uncertainty level. The framework is demonstrated on a new and on an existing reinforced concrete bridge deck impacted by corrosion deterioration. The results show that the framework can reduce the number of inspections by 50% compared to conventional scheduling methods, and the uncertainty regarding the bridge maintenance time is reduced by 16%.
Highlights
Introduction and PurposeIn the United States (U.S.), bridge inspections are conducted based on the NationalBridge Inspection Standards (NBIS), which were developed by the Federal HighwayAdministration (FHWA) after the collapse of the Silver Bridge in 1967 [1]
The objective of this study is to provide a systematic approach for integrating information from bridge condition prediction models, nondestructive evaluation (NDE) inspection data, and expert judgement to enhance the understanding of bridge condition, allowing for a more efficient use of inspection resources and better decision making about maintenance activities
To use Bayesian updating in selecting the suitable inspection method at the likelihood function of measuring aM (tIns), a pre-posterior analysis can be conducted, by (1) assuming different values of aM and different inspection scenarios (i.e., NDE methods); (2) establishing the posterior values for each different inspection outcome; and (3) updating the information regarding the condition of the bridge and to transition (TTT), according to each assumed or available inspection scenario
Summary
Introduction and PurposeIn the United States (U.S.), bridge inspections are conducted based on the NationalBridge Inspection Standards (NBIS), which were developed by the Federal HighwayAdministration (FHWA) after the collapse of the Silver Bridge in 1967 [1]. The NBIS requires that, for almost all bridges, a routine inspection should be conducted every two years using visual inspection, and for structurally deficient bridges, annual inspections should be conducted [2]. This uniform calendar-based approach was established based on expert judgement 50 years ago without any quantitative justification [3] and several limitations have been reported in the literature [4]. The uniform calendar-based approach does not consider the inspection requirements of a bridge based on its age and deterioration process, which can result in the same inspection interval and procedure for a new or aging bridge [5]. Given the limited information that can be collected from visual inspection, if the inspector suspects a problem with the bridge during routine inspections, Departments
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