TY - GEN
T1 - Point-based pomdp solvers for life-cycle cost minimization of deteriorating structures
AU - Papakonstantinou, K. G.
AU - Andriotis, C.
AU - Shinozuka, M.
N1 - Publisher Copyright:
© 2017 Taylor & Francis Group, London.
PY - 2017
Y1 - 2017
N2 - Optimized maintenance of operating aging infrastructures is of paramount importance to ensure safe and cost effective operation during their original design lifetime and even beyond that. Modern answers to the problem should focus on automated planning and decision making techniques taking advantage of informative but uncertain data that become available during the structural life-cycle. In this paper such a solution framework is presented, based on partially observable Markov decision processes (POMDPs). In a POMDP framework, the evolution of the system is described by stochastic processes, real-time observation data update the system state estimations, and all possible future actions, about where, when and what type of inspection and repair should be performed, are taken into account in order to optimize the long-term life-cycle objectives. As a consequence of their advanced mathematical attributes, POMDP models are unfortunately hard to solve. In recent years, however, significant breakthroughs have been achieved, mainly due to the introduction of point-based value iteration algorithms. In this work, several POMDP point-based methods are examined, with various characteristics in the selection of the belief space points/subset and the value function update procedures. To investigate the strengths and limitations of the various solution methods for structural maintenance problems of deteriorating infrastructure and to draw conclusions regarding their efficiency and applicability to problems of this kind, a realistic nonstationary example is selected, concerning corrosion of reinforcing bars of concrete structures in a spatial stochastic context.
AB - Optimized maintenance of operating aging infrastructures is of paramount importance to ensure safe and cost effective operation during their original design lifetime and even beyond that. Modern answers to the problem should focus on automated planning and decision making techniques taking advantage of informative but uncertain data that become available during the structural life-cycle. In this paper such a solution framework is presented, based on partially observable Markov decision processes (POMDPs). In a POMDP framework, the evolution of the system is described by stochastic processes, real-time observation data update the system state estimations, and all possible future actions, about where, when and what type of inspection and repair should be performed, are taken into account in order to optimize the long-term life-cycle objectives. As a consequence of their advanced mathematical attributes, POMDP models are unfortunately hard to solve. In recent years, however, significant breakthroughs have been achieved, mainly due to the introduction of point-based value iteration algorithms. In this work, several POMDP point-based methods are examined, with various characteristics in the selection of the belief space points/subset and the value function update procedures. To investigate the strengths and limitations of the various solution methods for structural maintenance problems of deteriorating infrastructure and to draw conclusions regarding their efficiency and applicability to problems of this kind, a realistic nonstationary example is selected, concerning corrosion of reinforcing bars of concrete structures in a spatial stochastic context.
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M3 - Conference contribution
AN - SCOPUS:85018645330
SN - 9781138028470
T3 - Life-Cycle of Engineering Systems: Emphasis on Sustainable Civil Infrastructure - 5th International Symposium on Life-Cycle Engineering, IALCCE 2016
SP - 427
EP - 434
BT - Life-Cycle of Engineering Systems
A2 - Bakker, Jaap
A2 - Frangopol, Dan M.
A2 - van Breugel, Klaas
PB - CRC Press/Balkema
T2 - 5th International Symposium on Life-Cycle Engineering, IALCCE 2016
Y2 - 16 October 2016 through 20 October 2016
ER -