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In order to ensure structural safety, deteriorating bridges should be maintained above their target reliability level. This level affects the optimum maintenance interventions during the s...

This paper proposes a generalized probabilistic framework for optimum inspection and maintenance planning of deteriorating structures. The proposed framework covers (1) the damage occurrence and propagation and service life prediction under uncertainty, (2) the relation between degree of damage and probability of damage detection of an inspection method, and (3) the effects of inspection and maintenance on service life and life-cycle cost. Optimum inspection and maintenance types and times are obtained through an optimization formulation by maximizing the expected service life and minimizing the expected total life-cycle cost consisting of inspection and maintenance costs. The service life, life-cycle cost, and maintenance delay, along with inspection and maintenance actions, are formulated using a decision tree model. The selection of the appropriate maintenance type depends on the degree of damage. The proposed framework is general and can be applied to any types of deteriorating structures or materials. Applications of the proposed framework may include, but are not limited to, bridges, buildings, aircrafts, and naval ships.

Repair actions are generally used to prevent failures and to maintain or extend the lifetime of deteriorating structures. To establish optimum inspection/repair planning for a structure, its performance under uncertainty should be assessed and predicted using probabilistic concepts and methods. This paper presents such an approach applied to deteriorating reinforced concrete (RC) structures under pitting corrosion. The inspection/repair plan is obtained from a probabilistic optimization process. Formulation of the extended lifetime for a given number of inspections is based on a decision tree analysis considering probability of damage detection. A damage detectability function is used to quantify the relation among damage intensity, quality of an inspection method, and probability of damage detection. The probability of repair representing the decision maker’s willingness to make a repair is also considered in the decision analysis. Effects of probabilities of inspection/repair and number of inspections on extended lifetime of an RC structure are investigated. To establish a cost-effective inspection/repair planning process, the formulation proposed is a biobjective optimization problem under uncertainty considering maximization of expected extended lifetime and minimization of expected total cost. The proposed approach is applied to an existing bridge.

Inspection and monitoring of deteriorating structures are generally performed to assess structural integrity and predict the remaining service life. For deteriorating structures, a reliable prediction of damage occurrence and the time-dependent damage evaluation depend on the accuracy of deterioration models under uncertainty. A probabilistic approach to establish optimum cost-based inspection and monitoring schedules of fatigue-sensitive structures is proposed in this paper. The inspection schedule is the solution of an optimization problem to minimize the expected total cost, including the costs of inspections or monitorings, and the expected failure cost. The failure criterion uses damage detection time and time for damage to reach the critical state. The uncertainties associated with damage occurrence/propagation and damage detection time are both considered. The solution of the optimization provides the inspection times and quality of inspections. The formulation of the optimization for inspection scheduling is extended to monitoring scheduling. Effects of the failure cost on inspection and monitoring scheduling are also studied. The proposed approach is applied to a fatigue-sensitive structure.

The state of a structural system subjected to deterioration processes is changing continuously. This state cannot be reliably predicted without considering both aleatory and epistemic uncertainties. To reduce the epistemic uncertainty, inspection and structural health monitoring (SHM) should be performed, and the performance prediction model should be updated periodically. Continuous monitoring is needed to reliably assess and predict the performance of structures. However, due to limited financial resources, continuous monitoring is not practical. Therefore, a cost-effective SHM strategy is necessary. In this paper, the probability that the performance prediction model based on monitoring data is usable in the future is computed by using the statistics of extremes and availability theory. This probability represents the availability of the monitoring data over nonmonitoring periods. The monitoring cost and availability can be found by solving a biobjective optimization problem. This problem consists in simultaneously minimizing the total monitoring cost and maximizing the availability of the monitoring data for performance prediction. Pareto solutions associated with monitoring duration and prediction duration are obtained. The proposed approach is applied to an existing bridge.

Based on the long-term monitored strain data induced by heavy vehicle traffic on an existing bridge, this paper presents an efficient approach to assessing the bridge system performance through a series-parallel system model consisting of bridge component reliabilities. The correlations among the bridge component safety margins are obtained by using actual traffic and strain data from structural health monitoring SHM. The prediction of bridge system reliability in the future is dependent on the performance functions of components. Sensitivity studies with respect to system modeling, correlations, extreme value probability distributions, measurement errors, and number of observations are carried out. A case study of the proposed approach is provided on an existing highway bridge in Wisconsin, which was monitored in 2004 by the Advanced Technology for Large Structural Systems Center, a National Engineering Research Center at Lehigh University, Bethlehem, Pa, USA. This study provides a solid basis for integrating SHM data into practical assessment of bridge system performance. DOI: 10.1061/ASCEST.1943-541X.0000014 CE Database subject headings: Bridges; Structural reliability; Structural safety; Monitoring; Assessments.

During the past decade, monitoring concepts for structural systems have been subjected to a rapid development process. They have become more and more important in the intervention planning (e.g., maintenance, repair, rehabilitation, replacement) on new and existing structures. Nevertheless, there is still a strong need for the efficient use of structural monitoring data in the reliability assessment and prediction models. Updating prediction models, based on monitoring data, affect the intervention strategies. Since these strategies involve costs, monitoring systems assist the efficient spending of available budgets. Therefore, the demand for the efficient use of monitoring data is not only related to structural reliability, but also to cost aspects. In an extended sense, structural monitoring can be considered similar to quality assurance and acceptance sampling, since it is not practically possible to continuously monitor all performance indicators in all critical sections of an entire structural system...