Modeling of risk-based inspection, maintenance and life-cycle cost with partially observable Markov decision processes

Corotis, Ross B.; Ellis, J.; Jiang, Mingxiang

doi:10.1080/15732470412331289305

Cited by 85 publications

(43 citation statements)

References 37 publications

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“…While for more traditional engineering analysis these probabilities may be derived from physical tests of systems (such as bridge surface condition (Corotis et al, 2005)), these types of tests do not exist at the technology-society nexus. In particular, research in developing communities is commonly plagued by insufficient data, as there are rarely repositories of infrastructure (or other) data available.…”

Section: Methodsmentioning

confidence: 99%

The fourth pillar of infrastructure sustainability: tailoring civil infrastructure to social context

Kaminsky

2015

Construction Management and Economics

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This research proposes technical performance over time as a fourth pillar of sustainability theory for infrastructure. It also describes a method that allows us to discover how changes in the technical pillar (operationalised as reduced breakage rates) may moderate the influence of the social pillar (operationalised as repair rates) on sanitation infrastructure outcomes. Oral histories were used to develop a history of sanitation for each of 152 poor households in four rural communities in Bangladesh that have gained access to sanitation in the past decade. Transcriptions and qualitative coding identified reported states of sanitation (for example, broken vs. functional) at three time steps. These were used to develop an initial vector and transition matrix for a Markov chain analysis. The breakage rate in this model was then adjusted to investigate the impact of improved technical durability on sanitation outcomes. For the case analyzed here, we found that increasing infrastructure durability by 50% (an estimated increase of two years) increased the rate of functional sanitation system use at model convergence from 54% to 88%. Increases in durability also caused households to use private rather than shared systems. Beyond this specific case, the generalisable theory and method presented here are analytic tools that permit targeted technical accommodation of social contexts specific to individual project sites.

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Section: Methodsmentioning

confidence: 99%

The fourth pillar of infrastructure sustainability: tailoring civil infrastructure to social context

Kaminsky

2015

Construction Management and Economics

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“…POMDP has previously been applied to planning of inspections in deteriorating structure and infrastructure (e.g. Madanat 1993;Corotis et al 2005;Papakonstantinou and Shinozuka 2014;Memarzadeh and Pozzi 2016). A special case of this model arises when the demand at any time can be observed with certainty.…”

Section: Sequential Decision Analysismentioning

confidence: 99%

Optimizing Adaptable Systems for Future Uncertainty

Straub

Špačková

2016

14th International Probabilistic Workshop

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Demands on structures and infrastructures change over their service life and cannot be predicted with certainty. Adaptable (or flexible) infrastructure designs are thus potentially beneficial, enabling easier adjustments of the systems at a later stage. However, systematic quantitative investigations and corresponding recommendations are missing. In (Špačková and Straub 2016), we present a framework for such an analysis, which is based on sequential decision processes. In this contribution, we summarize the approach and focus on the interpretation of flexibility. We show that the framework enables quantification of the value of flexibility, to answer the question: what is the maximum amount that should be spent additionally to ensure system flexibility? Two case studies illustrate that this value is strongly dependent on a number of factors, in particular on the types of uncertainty present and the amount of future information collected in the future.

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“…In an early work, Madanat & Ben-Akiva (1994) adopt POMDPs for decision-making for highway-pavement networks. Ellis et al (1995), and Corotis et al (2005) demonstrate use of POMDPs for bridge inspection planning. Papakonstantinou & Shinozuka (2014a, 2014b provide a thorough overview of solvers suited for solution of large-scale and more realistic problems.…”

Section: Pomdp Implementation In Infrastructure Managementmentioning

confidence: 99%

Observation-based Decision-making for Infrastructure

Chatzi¹,

Παπακωνσταντίνου²,

Hajdin³

et al. 2017

Proceedings of the Joint COST TU1402 - COST TU1406 - IABSE WC1 Workshop: The Value of Structural Health Monitoring for the Reli

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Abstract. The aim of this review paper is to summarize available options for decision-support toward the optimal management of infrastructure systems under consideration of uncertainties. Thereafter, we elaborate on a particular variant, namely Partially Observable Markov Decision Processes (POMDP), as a flexible framework for the incorporation of information from observations (visual inspections, non-destructive testing and monitoring) in the context of optimal inspection and maintenance planning. Examples from the literature are presented, demonstrating the potential of planning under uncertainty and the links to the Value of Information (VoI) from Structural Health Monitoring (SHM).

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Modeling of risk-based inspection, maintenance and life-cycle cost with partially observable Markov decision processes

Cited by 85 publications

References 37 publications

The fourth pillar of infrastructure sustainability: tailoring civil infrastructure to social context

The fourth pillar of infrastructure sustainability: tailoring civil infrastructure to social context

Optimizing Adaptable Systems for Future Uncertainty

Observation-based Decision-making for Infrastructure

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