Search

Bridge management is a complex engineering issue with public safety and financial implications. It is a challenge to provide efficient bridge management systems, taking into account uncertainties in the structural degradation process, the loads and resistance of the structure, the decisions that are made concerning assessment strategies—such as inspections or structural health monitoring (SHM)—and maintenance and rehabilitation strategies. It is important to determine optimal management strategies that satisfy budget constraints, because efficient use of available financial funds is a primary objective of stakeholders. The purpose of this paper is to provide a probabilistic framework based on SHM results for optimization of management strategies under multiple criteria, including budget availability. To take into account uncertainties in the decision process, an event tree–based approach is used. This enables quantification of the expected inspection, SHM, maintenance and failure costs, and accuracy of the decision process according to the occurrence and duration of future SHM programs. To include budget constraints, the probability distribution of management cost—defined as the sum of inspection, SHM, and maintenance costs—is determined at each decision time and compared with an ideal budget distribution. The proposed approach is applied to an existing bridge.

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 I-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.

Visual inspections, diagnostic instrumentation and monitoring of bridges are essential for determination of intervention planning. Nowadays, a great challenge is to use bridge monitoring to determine optimal maintenance strategies. Indeed, monitoring data allow updating the knowledge on the structural performance and can significantly affect the intervention strategy. A methodology for structural reliability analysis of superstructures of steel girder bridges incorporating monitoring results is presented. Component reliability indices are computed for the slab and the girders and a time-variant performance analysis is conducted by considering corrosion. This allows dealing with structural capacity and serviceability with respect to permanent deformation under overload. Continuous and simultaneous measurements at discrete points of the bridge superstructure, provided by monitoring, allow to update some parameters of the serviceability limit state function and to perform a fatigue reliability analysis for several bridge components. The consideration of multiple limit states can lead to different optimal maintenance solutions, if taken individually or simultaneously. The aim of this paper is to show the impact of considering a global approach rather than an individual one when determining optimal maintenance strategies associated with several bridge limit states. The approach is illustrated on an existing highway bridge.