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Optimal Infrastructure System Maintenance and Repair Policies with Random Deterioration Model Parameters

Abstract

Accurate facility deterioration models are important inputs for the selection of Infrastructure Maintenance, Repair, and Reconstruction (MR & R) policies. Deterioration models are developed based on expert judgment or empirical observations. These resources, however, might not be sufficient to accurately represent the performance of infrastructure facilities. Incorrect deterioration models may lead to wrong predictions of infrastructure performance and selection of inappropriate MR & R policies. This results in higher lifecycle costs. Existing infrastructure MR & R decisionmaking models assume that deterioration models represent the real deterioration process of infrastructure facilities. This assumption ignores the uncertainty in empiricallyderived facility deterioration models. This dissertation presents a methodology for selecting MR & R policies for systems of infrastructure facilities under uncertainty in the deterioration model parameters. It is assumed that inspections reveal the true conditions of facilities. Based on the inspection results, the deterioration model parameters can be updated to express the deterioration process more accurately. It is expected that more appropriate maintenance policies will be selected as a result. In the first part of this dissertation, it is assumed that facility inspections are performed at the beginning of every year. The model parameters are updated and MR & R policies are selected every year using the updated deterioration models. In the second part, the assumption is relaxed and alternate inspection frequencies are considered. In this case, the updates of the model parameters and the selection of optimal MR & R policies are executed only after an inspection. The results of the parametric analyses demonstrate that updating the deterioration models reduces the expected system costs. The results also show that relaxing the facility inspection frequency can reduce the total costs further.

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