Value of information-based decision analysis of the optimal next inspection type for deteriorating structural systems

Haladuick, Shane; Dann, Markus R.

doi:10.1080/15732479.2018.1433692

Cited by 12 publications

(6 citation statements)

References 27 publications

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“…However, to calculate the VoI, long-term projections of the expected outcomes from different inspection and maintenance scenarios have to be conducted, which can cause several inaccuracies and uncertainties if these projections are made for a long time in the future. Thus, without having to consider the whole life cycle of the system, Haladuick and Dann [39] presented a framework that uses the VoI to help inspection planners in selecting the inspection technique for only the next inspection time.…”

Section: Risk-based Methodsmentioning

confidence: 99%

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A Comprehensive Uncertainty-Based Framework for Inspection Planning of Highway Bridges

2021

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Bridge inspection standards in the United States require routine visual inspections to be conducted on most bridges at a maximum interval of two years regardless of the bridge condition. Limitations of this uniform calendar-based approach have been reported in the literature. Accordingly, the objective of this study is to provide a new systematic approach for inspection planning that integrates information from bridge condition prediction models, inspection data, and expert opinion using Bayesian analysis to enhance inspection efficiency and maintenance activities. The uncertainty-based inspection framework proposed in this study can help bridge owners avoid unnecessary or delayed inspections and repair actions, determine the inspection method, and consider more than one deterioration process or bridge component during the inspection planning process. The inspection time and method are determined based on the uncertainty and risks associated with the bridge condition. As uncertainty in the bridge condition reaches a defined threshold, an inspection is scheduled utilizing nondestructive techniques to reduce the uncertainty level. The framework is demonstrated on a new and on an existing reinforced concrete bridge deck impacted by corrosion deterioration. The results show that the framework can reduce the number of inspections by 50% compared to conventional scheduling methods, and the uncertainty regarding the bridge maintenance time is reduced by 16%.

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Section: Risk-based Methodsmentioning

confidence: 99%

“…Overall, Equation 3 ,  e )) [42]. The higher the accuracy of an NDE, the lower  e will be, which will provide measurements closer to the actual deterioration process, providing a higher reduction in the uncertainty and enhancing the model prediction [39].…”

Section: Accuracy Of Inspection Methods and Data Obtained During Inspmentioning

confidence: 99%

A Comprehensive Uncertainty-Based Framework for Inspection Planning of Highway Bridges

2021

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“…21 Indeed, the process of acquiring information (here specifically consisting in measurements from sensors) may not always be justified because of the high cost, and one finds this out only after (the information is acquired by the measurements taken). 22,23 In other words, VoI predicts (by simulation) the economic benefit of collecting measurements in specific sensors locations, by accounting (in addition to the cost of the sensor and measurement acquisition chain) for the costs that the decision-maker might incur when adopting mitigative actions to counteract SSC degradation (or its failure, in case no action is taken) based on the collected measurements. This provides a powerful tool for comparing different locations of measurement before physically placing the sensors on the SSCs (i.e.…”

Section: Value Of Informationmentioning

confidence: 99%

Optimal sensor positioning on pressurized equipment based on Value of Information

Hoseyni

Maio

Zio

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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In this work, we apply a simulation-based framework that makes use of the Value of Information (VoI) for identifying the optimal spatial positioning of sensors on pressurized equipment. VoI is a utility-based Figure of Merit (FoM) which quantifies the beneﬁts/losses of acquiring information. Sensors are typically positioned on pressurized equipment in line with specific recommendations based on operational experience, like UNI 11096 in Italy. We show that the recommendations in UNI 11096 are, indeed, justified and that, incidentally, relying on VoI for the optimization of the sensor positioning, one can achieve the same monitoring performance, as measured by VoI, where following UNI 11096, but with a reduced number of sensors. The proposed VoI-based approach can, thus, be used to confirm or revise recommendations coming from operational experience.

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“…Additional information reduces the uncertainty affecting decision making, leading to improved decisions (if available). Over the past few years, VoI algorithms and decision analysis have been applied to quantifying the expected values of inspection or monitoring activities for deteriorating structures (Straub 2014, Haladuick and Dann 2018, Thöns 2018, to optimising maintenance decisions (Zou et al 2019, Zitrou, Bedford, and Daneshkhah 2013, Huynh, Barros, and Bérenguer 2012, to scheduling inspections (Memarzadeh andPozzi 2016, Irman, Thöns, andLeira 2017) and to optimising sensor placement (Malings and Pozzi 2016), etc. There is however a lack of a comparative study on VoI, LCC and reliability based maintenance optimization approaches, which would contribute to better understanding of the differences between these approaches.…”

Section: Introductionmentioning

confidence: 99%

Fatigue inspection and maintenance optimization: A comparison of information value, life cycle cost and reliability based approaches

et al. 2021

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Fatigue cracks increase structural failure risk and timely maintenance is very important. Maintenance planning is often formulated as a probabilistic optimization problem, considering uncertainties in structural and load modelling, material properties, damage measurements, etc. A decision rule or strategy, e.g. condition based maintenance (CBM), needs to be set up, and then an optimal maintenance criterion or threshold is derived via solving the optimization problem. This paper develops a probabilistic maintenance optimization approach exploiting value of information (VoI) and Bayesian decision optimization. The VoI based approach explicitly quantifies added values from future inspections, and gives an optimal decision (or strategy) by direct modelling decision alternatives and evaluating their expected outcomes, rather than a pre-defined strategy. A comparative study on VoI, life cycle cost (LCC) and reliability based optimization approaches is conducted. It is shown that the VoI based approach takes all available maintenance strategies into account (both with and without inspections), and can reliably yield optimal maintenance strategies, whether the VoI is larger than or equal to zero. When the VoI is equal to zero, LCC and reliability based CBM optimization can result in suboptimal maintenance strategies. The differences in the approaches are illustrated on fatiguesensitive components in a marine structure.

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Value of information-based decision analysis of the optimal next inspection type for deteriorating structural systems

Cited by 12 publications

References 27 publications

A Comprehensive Uncertainty-Based Framework for Inspection Planning of Highway Bridges

A Comprehensive Uncertainty-Based Framework for Inspection Planning of Highway Bridges

Optimal sensor positioning on pressurized equipment based on Value of Information

Fatigue inspection and maintenance optimization: A comparison of information value, life cycle cost and reliability based approaches

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