Bayesian Based Strategies for Inspection And Maintenance to Optimize Structural Life-Cycle Performance

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A nation's civil infrastructure has significant impact on the safety, quality of life and economy. It is important that the infrastructure, such as bridges, be maintained so they can carry the intended traffic safely. Currently, bridges are inspected visually every two years. This procedure is expensive and uncertain (this was evident in the collapse of I-35 bridge in 2007). Even after inspection or after non-destructive testing, decision has to be made as to what extent the bridge needs to be repaired, abandoned, or destroyed. New mathematical tools can be developed that will assist in this decision making process. The objective of this work therefore is to approach the aging problem through a suitable modeling framework, advance the available computational tools for sustainable management and allocation of resources for degrading civil structures and respond effectively to critical, challenging societal demands for safer infrastructure at a minimum cost.Markov Decision Processes (MDPs) have a long, successful history of implementation in risk management and minimum life-cycle costing of civil engineering structures. The successful reliance of infrastructure management projects on MDPs is mainly due to the facts that MDPs advice the decision-maker to make optimum sequential decisions based on the actual inspection or non-destructive testing he/she performs, the ease in which extensive amount of inspection data can be incorporated in the mathematical framework, and finally the existence of comprehensive studies on these methods in a variety of scientific fields. MDPs are controlled stochastic processes in which a decision-maker is uncertain about the exact effect of executing a certain action. Research plan includes partially optimized MDP formulation of corrosion deterioration process, evaluation of the algorithm for infrastructure management, and formulation of decision making tool. Field data from CalTran will be used to validate the algorithm. The methodology will rely on stochastic control methods and Bayesian principles in order to plan an optimum life-cycle policy and perform inspection and maintenance actions based on the actual structural conditions.