Value of structural health information in partially observable stochastic environments

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

doi:10.1016/j.strusafe.2020.102072

Cited by 18 publications

(15 citation statements)

References 51 publications

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“…In line with this sentiment, Ref. [34] showed that solutions of the POMDPs optimisation problem can be a powerful and rigorously proven method in identifying the value of structural health monitoring information and optimal maintenance strategies.…”

Section: Decision Support Systemsmentioning

confidence: 88%

A New Decision Process for Choosing the Wind Resource Assessment Workflow with the Best Compromise between Accuracy and Costs for a Given Project in Complex Terrain

et al. 2022

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In wind energy, the accuracy of the estimation of the wind resource has an enormous effect on the expected rate of return of a project. For a given project, the wind resource assessor is faced with a difficult choice of a wide range of simulation tools and workflows with varying accuracies (or “skill”) and costs. There is currently no guideline or process available in the industry for helping with the decision of the most “optimal” choice—and this is particularly challenging in mountainous (or “complex”) terrain. In this work, a new decision process for selecting the Wind Resource Assessment (WRA) workflow that would expect to deliver the best compromise between skill and costs for a given wind energy project is developed, with a focus on complex terrain. This involves estimating the expected skill and cost scores using a set of pre-defined weighted parameters. The new process is designed and tested by applying seven different WRA workflows to five different complex terrain sites. The quality of the decision process is then assessed for all the sites by comparing the decision made (i.e., choice of optimal workflow) using the expected skill and cost scores with the decision made using the actual skill and cost scores (obtained by comparing measurements and simulations at a validation location). The results show that the decision process works well, but the accuracy decreases as the site complexity increases. It is therefore concluded that some of the parameter weightings should be dependent on site complexity. On-going work involves collecting more data from a large range of sites, implementing measures to reduce the subjectivity of the process and developing a reliable and robust automated decision tool for the industry.

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Section: Decision Support Systemsmentioning

confidence: 88%

A New Decision Process for Choosing the Wind Resource Assessment Workflow with the Best Compromise between Accuracy and Costs for a Given Project in Complex Terrain

et al. 2022

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“…Typically, in the current approach to infrastructure O&M, intermittent visual inspection schemes are adopted by operators, with targeted non-destructive evaluations also complementing inspection when needed [5]. Therefore, to demonstrate the potential benefit of deploying continuous SHM systems on structures, as compared against the typical case of intermittent visual inspections, a more specialized metric, the Value of Structural Health Monitoring (VoSHM) [32], can be introduced:…”

Section: Bayesian Decision Analysis For the Quantification Of The Voshmmentioning

confidence: 99%

A framework for quantifying the value of vibration-based structural health monitoring

Kamariotis¹,

Chatzi²,

Straub³

2022

Preprint

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The difficulty in quantifying the benefit of Structural Health Monitoring (SHM) for decision support is one of the bottlenecks to an extensive adoption of SHM on real-world structures. In this paper, we present a framework for such a quantification of the value of vibration-based SHM, which can be flexibly applied to different use cases. These cover SHM-based decisions at different time scales, from near-real time diagnostics to the prognosis of slowly evolving deterioration processes over the lifetime of a structure. The framework includes an advanced model of the SHM system. It employs a Bayesian filter for the tasks of sequential joint deterioration state-parameter estimation and structural reliability updating, using continuously identified modal and intermittent visual inspection data. It also includes a realistic model of the inspection and maintenance decisions throughout the structural life-cycle. On this basis, the Value of SHM is quantified by the difference in expected total life-cycle costs with and without the SHM. We investigate the framework through application on a numerical model of a two-span bridge system, subjected to gradual and shock deterioration, as well as to changing environmental conditions, over its lifetime. The results show that this framework can be used as an a-priori decision support tool to inform the decision on whether or not to install a vibration-based SHM system on a structure, for a wide range of SHM use cases.

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“…We can define the step-wise Value of Information (VoI) associated with a certain policy and a certain inspection action aI as [62]:…”

Section: To Observe or Not? Value Of Information In Pomdpsmentioning

confidence: 99%

“…Elaborating on Eq. ( 8), and considering the fact that inspections do not change the state of the system, we have [62]:…”

Section: To Observe or Not? Value Of Information In Pomdpsmentioning

confidence: 99%

“…Following the above, the concavity of the POMDP value function of Eq. ( 8), and the properties of the Bellman contraction operator, we can show that step-wise VoI, as well as VoI over the life-cycle, are always non-negative if the decisions follow the POMDP optimal policy [62,63]. At the extreme case that no inspection means no information at all, VoI reaches its highest value.…”

Section: To Observe or Not? Value Of Information In Pomdpsmentioning

confidence: 99%

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Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints

Andriotis

Παπακωνσταντίνου

2021

Reliability Engineering & System Safety

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Determination of inspection and maintenance policies for minimizing long-term risks and costs in deteriorating engineering environments constitutes a complex optimization problem. Major computational challenges include the (i) curse of dimensionality, due to exponential scaling of state/action set cardinalities with the number of components; (ii) curse of history, related to exponentially growing decision-trees with the number of decisionsteps; (iii) presence of state uncertainties, induced by inherent environment stochasticity and variability of inspection/monitoring measurements; (iv) presence of constraints, pertaining to stochastic long-term limitations, due to resource scarcity and other infeasible/undesirable system responses. In this work, these challenges are addressed within a joint framework of constrained Partially Observable Markov Decision Processes (POMDP) and multi-agent Deep Reinforcement Learning (DRL). POMDPs optimally tackle (ii)-(iii), combining stochastic dynamic programming with Bayesian inference principles. Multi-agent DRL addresses (i), through deep function parametrizations and decentralized control assumptions. Challenge (iv) is herein handled through proper state augmentation and Lagrangian relaxation, with emphasis on life-cycle risk-based constraints and budget limitations. The underlying algorithmic steps are provided, and the proposed framework is found to outperform wellestablished policy baselines and facilitate adept prescription of inspection and intervention actions, in cases where decisions must be made in the most resource-and risk-aware manner.

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Value of structural health information in partially observable stochastic environments

Cited by 18 publications

References 51 publications

A New Decision Process for Choosing the Wind Resource Assessment Workflow with the Best Compromise between Accuracy and Costs for a Given Project in Complex Terrain

A New Decision Process for Choosing the Wind Resource Assessment Workflow with the Best Compromise between Accuracy and Costs for a Given Project in Complex Terrain

A framework for quantifying the value of vibration-based structural health monitoring

Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints

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