Researchers at the Technical University of Denmark, including P. G. Morato, K. G. Papakonstantinou, C. P. Andriotis, J. S. Nielsen, and P. Rigo, have developed a novel approach to optimize inspection and maintenance planning for deteriorating structural components in civil and maritime engineering systems. Their work, published in a recent paper, combines dynamic Bayesian networks with Partially Observable Markov Decision Processes (POMDPs) to address the complex decision-making challenges under uncertainty that these systems face throughout their operational life.
The researchers highlight that structures such as bridges, offshore platforms, and wind turbines are exposed to deterioration mechanisms like fatigue and corrosion. Managing these systems efficiently requires solving a complex sequential decision-making problem aimed at controlling the risk of structural failures. Traditional risk-based inspection planning methodologies often rely on dynamic Bayesian networks and pre-defined heuristic decision rules to simplify this problem. However, these methods may be limited by the narrow scope of the decision rules, potentially compromising the effectiveness of the resulting policies.
To overcome these limitations, the researchers propose using POMDPs, a principled mathematical methodology for stochastic optimal control under uncertain action outcomes and observations. POMDPs prescribe optimal actions based on the entire, dynamically updated state probability distribution, providing a more comprehensive approach to decision-making. The paper details the formulation for developing both infinite and finite horizon POMDPs within a structural reliability context.
The proposed methodology was implemented and tested on a structural component subject to fatigue deterioration. The researchers used state-of-the-art point-based POMDP solvers to tackle the underlying planning optimization problem. Through numerical experiments, they compared POMDP-based policies with heuristic-based policies. The results demonstrated that POMDPs achieve substantially lower costs compared to traditional methods, even in conventional problem settings.
This research underscores the potential of POMDPs to revolutionize inspection and maintenance planning for deteriorating structures. By integrating dynamic Bayesian networks with POMDPs, the researchers have provided a robust framework that can significantly enhance the efficiency and cost-effectiveness of structural management in civil and maritime engineering. The findings suggest that adopting POMDP-based approaches could lead to better risk management and prolonged structural integrity, ultimately benefiting industries reliant on these critical infrastructures. Read the original research paper here.