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Effective life-cycle management of deteriorating structures should be based on an accurate and reliable damage propagation prediction model under uncertainty. Appropriate updating with inspected in...

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...

The performance and remaining service life of deteriorating reinforced concrete (RC) structures decrease with time due to increased critical damage propagation based on coupled corrosion and fatigue. Since damage propagation prediction is highly uncertain, an updating process after each inspection and monitoring is required to improve the accuracy and applicability of the structural performance and service life prediction. This paper proposes a probabilistic approach for updating the service life prediction of deteriorating structures under coupled corrosion-fatigue deterioration processes. The probabilistic parameters involved in the coupled corrosion-fatigue deterioration model are updated using Bayesian inference (BI). In the updating process, the Markov Chain Monte Carlo (MCMC) sampling method is applied to integrate all the possible inspection results associated with no damage detection, corrosion damage, and/or fatigue crack damage. The most appropriate updating parameters of the deterioration model are determined by adopting a comparison-based analysis. Finally, the remaining service life of the deteriorating structures based on the updated coupled damage prediction model is re-estimated. The application of the proposed probabilistic approach is illustrated on an RC bridge girder. This paper shows that (a) the coupled corrosion-fatigue damage can reduce the structural service life compared to corrosion or fatigue damage separately, and (b) the errors and uncertainty in corrosion-fatigue damage prediction can be reduced through the parameter updating integrating multiple inspection outcomes.

Optimum inspection and maintenance planning is essential for the successful management of deteriorating structures. The reliability and accuracy of inspection and maintenance planning can be significantly improved by integrating new information obtained from inspections. For this reason, the inspection and maintenance planning should include an updating process after each inspection. This paper presents such a probabilistic approach for optimum inspection and maintenance planning. The proposed approach includes two multi-objective optimization (MOOP) processes before and after damage detection for effective updating. The first MOOP before damage detection is performed to determine the optimum inspection times by minimizing both the expected damage detection delay and expected total inspection cost. The fatigue crack detected at the inspection time scheduled from the first MOOP is used to update the probabilistic fatigue crack propagation. The updated crack propagation is applied to formulate the second MOOP, which determines the optimum times for maintenance with the objectives of minimizing both the expected maintenance delay and expected total inspection cost. The decision making process is applied to select the best Pareto solution from the Pareto optimal solutions of the first and second MOOPs. The proposed approach is applied to a fatigue critical detail of an existing steel bridge.