Maintenance planning under uncertainties using a continuous-state POMDP framework

Abstract

Planning under uncertainty is an area that has attracted significant attention in recent years. Partially Observable Markov Decision Process (POMDP) is a sequential decision making framework particularly suited for tackling this problem. POMDP has this far mainly been used in robotics for a discrete-state formulation. Only few authors have dealt with the solution of the continuous-state POMDPs. This paper introduces the concept of approximating the continuous state using a mixture of Gaussians in order to render this methodology suitable for the problem of optimal maintenance planning in civil structures. Presently, a large part of existing infrastructure is reaching the end of its expected lifespan. The POMDP framework is used herein in order to take deterioration processes into account and to accordingly plan the optimal maintenance strategy for the remaining lifespan. The capabilities of the method are demonstrated through an example application on a bridge structure.