Maintenance planning using continuous-state partially observable Markov decision processes and non-linear action models. Issue 8 (2nd August 2016)

• Record Type: Journal Article
• Authors: Schöbi, Roland; Chatzi, Eleni N.
• Is Part Of: Structure and infrastructure engineering. Volume 12:Issue 8(2016)
• Page Start: 977
• Page End: 994
• Publication Date: 2016-08-02
• Subjects: Life cycle assessment -- maintenance planning under uncertainty -- partially observable Markov decision processes (POMDPs) -- non-linear action models -- normalised unscented transform (NUT); Structural analysis (Engineering) -- Periodicals; Structural engineering -- Periodicals; Buildings -- Performance -- Periodicals; 620.005
• DOI: 10.1080/15732479.2015.1076485
• Languages: English
• ISSNs: 1573-2479

Abstract

Abstract : The signs of deterioration in worldwide infrastructure and the associated socio-economic and environmental losses call for sustainable resource management and policy-making. To this end, this work presents an enhanced variant of partially observable Markov decision processes (POMDPs) for the life cycle assessment and maintenance planning of infrastructure. POMDPs comprise a method, commonly employed in the field of robotics, for decision-making on the basis of uncertain observations. In the work presented herein, a continuous-state POMDP formulation is presented which is adapted to the problem of decision-making for optimal management of civil structures. The aforementioned problem may comprise non-linear and non-deterministic action and observation models. The continuous-state POMDP is herein coupled with a normalised unscented transform (NUT) in order to deliver a framework able to tackle non-linearities that likely characterise action models. The capabilities of this enhanced framework and its applicability to the maintenance planning problem are presented via two applications. In a first illustrative example, the use of the NUT is demonstrated within the framework of the value iteration algorithm. Next, the proposed continuous-state framework is compared against a discrete-state formulation for implementation on a life cycle assessment problem.