ArXiv Preprint
Structural Health Monitoring (SHM) describes a process for inferring
quantifiable metrics of structural condition, which can serve as input to
support decisions on the operation and maintenance of infrastructure assets.
Given the long lifespan of critical structures, this problem can be cast as a
sequential decision making problem over prescribed horizons. Partially
Observable Markov Decision Processes (POMDPs) offer a formal framework to solve
the underlying optimal planning task. However, two issues can undermine the
POMDP solutions. Firstly, the need for a model that can adequately describe the
evolution of the structural condition under deterioration or corrective actions
and, secondly, the non-trivial task of recovery of the observation process
parameters from available monitoring data. Despite these potential challenges,
the adopted POMDP models do not typically account for uncertainty on model
parameters, leading to solutions which can be unrealistically confident. In
this work, we address both key issues. We present a framework to estimate POMDP
transition and observation model parameters directly from available data, via
Markov Chain Monte Carlo (MCMC) sampling of a Hidden Markov Model (HMM)
conditioned on actions. The MCMC inference estimates distributions of the
involved model parameters. We then form and solve the POMDP problem by
exploiting the inferred distributions, to derive solutions that are robust to
model uncertainty. We successfully apply our approach on maintenance planning
for railway track assets on the basis of a "fractal value" indicator, which is
computed from actual railway monitoring data.
Giacomo Arcieri, Cyprien Hoelzl, Oliver Schwery, Daniel Straub, Konstantinos G. Papakonstantinou, Eleni Chatzi
2022-12-15
