Value of structural health information in partially observable stochastic environments

Andriotis, C. P.; Παπακωνσταντίνου, Κωνσταντίνος; Chatzi, Eleni

doi:10.48550/arxiv.1912.12534

Cited by 5 publications

(7 citation statements)

References 56 publications

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“…The first part of the right hand side of equation ( 21) can be computed in the following way. We draw samples θ (k) , k = 1, .., n MCS , from the prior distribution π pr (θ) and use them to compute the accumulated probability of failure via equation (14), and subsequently compute the hazard function with equation (15). When the hazard function exceeds the threshold, i.e.…”

Section: Computation Of the Expected Total Life-cycle Cost In The Pri...mentioning

confidence: 99%

“…In the civil and infrastructure engineering context, the computation of the VoI has been considered mainly related to optimal inspection planning for deteriorating structural systems [6,7,8]. Recent works [9,10,11,12,13,14,15,16] use the VoI concept in an attempt to quantify the value of SHM on idealized structural systems within a Bayesian framework. All works to date, however, adopt rather simplified assumptions regarding the type of information offered by the SHM system.…”

Section: Introductionmentioning

confidence: 99%

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Value of information from vibration-based structural health monitoring extracted via Bayesian model updating

Kamariotis,

Chatzi,

Straub

2021

Preprint

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Quantifying the value of the information extracted from a structural health monitoring (SHM) system is an important step towards convincing decision makers to implement these systems. We quantify this value by adaptation of the Bayesian decision analysis framework. In contrast to previous works, we model in detail the entire process of data generation to processing, model updating and reliability calculation, and investigate it on a deteriorating bridge system. The framework assumes that dynamic response data are obtained in a sequential fashion from deployed accelerometers, subsequently processed by an output-only operational modal analysis scheme for identifying the system's modal characteristics. We employ a classical Bayesian model updating methodology to sequentially learn the deterioration and estimate the structural damage evolution over time. This leads to sequential updating of the structural reliability, which constitutes the basis for a preposterior Bayesian decision analysis. Alternative actions are defined and a heuristicbased approach is employed for the life-cycle optimization. By solving the preposterior Bayesian decision analysis, one is able to quantify the benefit of the availability of long-term SHM vibrational data. Numerical investigations show that this framework can provide quantitative measures on the optimality of an SHM system in a specific decision context.

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Section: Computation Of the Expected Total Life-cycle Cost In The Pri...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Value of information from vibration-based structural health monitoring extracted via Bayesian model updating

Kamariotis,

Chatzi,

Straub

2021

Preprint

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“…Existing point-based solvers are able to solve large state space problems only for infinite horizon POMDPs [50]. However, an infinite horizon POMDP can be transformed to a finite horizon POMDP by augmenting the state space as proposed by [12,29,42]. In this case, the time must be encoded in the state space and a terminal state is required.…”

Section: Finite Horizon Pomdpsmentioning

confidence: 99%

“…Information about the condition of structural components can be gathered during their operational life through inspections or monitoring, allowing the decision maker to take more informed and rational actions [12]. However, both maintenance actions and observations are associated with certain costs which must be optimally balanced against the risk of structural failure.…”

Section: Introductionmentioning

confidence: 99%

Optimal Inspection and Maintenance Planning for Deteriorating Structural Components through Dynamic Bayesian Networks and Markov Decision Processes

Morato,

Papakonstantinou,

Andriotis

et al. 2020

Preprint

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Civil and maritime engineering systems, among others, from bridges to offshore platforms and wind turbines, must be efficiently managed as they are exposed to deterioration mechanisms throughout their operational life, such as fatigue or corrosion. Identifying optimal inspection and maintenance policies demands the solution of a complex sequential decision-making problem under uncertainty, with the main objective of efficiently controlling the risk associated with structural failures. Addressing this complexity, risk-based inspection planning methodologies, supported often by dynamic Bayesian networks, evaluate a set of pre-defined heuristic decision rules to reasonably simplify the decision problem. However, the resulting policies may be compromised by the limited space considered in the definition of the decision rules. Avoiding this limitation, Partially Observable Markov Decision Processes (POMDPs) provide a principled mathematical methodology for stochastic optimal control under uncertain action outcomes and observations, in which the optimal actions are prescribed as a function of the entire, dynamically updated, state probability distribution. In this paper, we combine dynamic Bayesian networks with POMDPs in a joint framework for optimal inspection and maintenance planning, and we provide the relevant formulation for developing both infinite and finite horizon POMDPs in a structural reliability context. The proposed methodology is implemented and tested for the case of a structural component subject to fatigue deterioration, demonstrating the capability of state-of-the-art point-based POMDP solvers for solving the underlying planning optimization problem. Within the numerical experiments, POMDP and heuristic-based policies are thoroughly compared, and results showcase that POMDPs achieve substantially lower costs as compared to their counterparts, even for traditional problem settings.

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“…[14] extends the VoI analysis to optimize not only static one-shot inspection, but also sequentially dependent observations. [13] and [1] apply the componentwise VoI metric to sequential decision making in the management of infrastructure systems, modeled by Partially Observable Markov Decision Process (POMDP). [21] uses decision trees to assess long-term VoI.…”

Section: Introductionmentioning

confidence: 99%

Optimal Inspection of Network Systems via Value of Information Analysis

Lin,

Song,

Pozzi

2021

Preprint

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This paper develops computable metrics to assign priorities for information collection on network systems made up by binary components. Components are worth inspecting because their condition state is uncertain and the system functioning depends on it. The Value of Information (VoI) allows assessing the impact of information in decision making under uncertainty, including the precision of the observation, the available actions and the expected economic loss. Some VoI-based metrics for system-level and component-level maintenance actions, defined as "global" and "local" metrics, respectively, are introduced, analyzed and applied to series and parallel systems. Their computationally complexity of applications to general networks is discussed and, to tame the complexity for the local metric assessment, a heuristic is presented and its performance is compared on some case studies.

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Value of structural health information in partially observable stochastic environments

Cited by 5 publications

References 56 publications

Value of information from vibration-based structural health monitoring extracted via Bayesian model updating

Value of information from vibration-based structural health monitoring extracted via Bayesian model updating

Optimal Inspection and Maintenance Planning for Deteriorating Structural Components through Dynamic Bayesian Networks and Markov Decision Processes

Optimal Inspection of Network Systems via Value of Information Analysis

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