Risk management of multi-state multi-component bridge systems using partially observable Markov decision processes Shafieezadeh, Abdollah; Fereshtehnejad, Ehsan
Infrastructure systems play a critical role in providing continuous services to societies. Exposure to stressors such as aging, demand loads, and environmental factors threatens the functionality and safety of infrastructure systems, highlighting the necessity for proper decision-making frameworks. Toward this goal, in the light of imperfect asset condition state evaluation, this paper presents a stochastic framework based on partially observable Markov decision process (POMDP) for the determination of optimal maintenance actions. A feature of this approach is its ability to effectively and accurately manage large scale, multi-state multi-component bridge systems. To overcome the dimensionality curse of the decision-making for such large systems without losing accuracy, the “counting process” state reduction technique is applied and conformed in a novel way. Further, to significantly reduce the computational runtime while keeping the accuracy in a high level, a randomized point-based value iteration POMDP is utilized. The proposed framework is applied to a case study bridge system with four steel girders and one concrete deck. Results of 12 random runs showed acceptable convergence in the optimized average expected long-run reward. The applied framework provides optimal policies for the concrete deck and girders in each of the possible states. It is also concluded that the combination of the POMDP decision-making framework and the “counting process” technique gives rise to an efficient and accurate approach for the optimal management of large scale systems.
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