Quantifying the Benefits of Structural Health Monitoring Using Value of Information and Decision Risk Modeling

2022 ◽  
pp. 21-24
Author(s):  
Mayank Chadha ◽  
Zhen Hu ◽  
Michael D. Todd
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Value Of Information ◽  
Risk Modeling ◽  
Structural Health

scholarly journals Value of Information of Structural Health Monitoring in Asset Management of Flood Defences

2019 ◽  
Vol 4 (3) ◽  
pp. 56 ◽  
Author(s):  
Wouter Jan Klerk ◽  
Timo Schweckendiek ◽  
Frank den Heijer ◽  
Matthijs Kok
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Asset Management ◽  
Value Of Information ◽  
Relative Magnitude ◽  
Practical Implementation ◽  
Structural Health ◽  
Monitoring Strategies ◽  
Efficient Measure ◽  
Flood Defence

One of the most rapidly emerging measures in infrastructure asset management is Structural Health Monitoring (SHM), which aims at reducing uncertainty in structural performance by using monitoring equipment. As earthen flood defence structures typically have large strength uncertainties, such techniques can be particularly promising. However, insight in the key characteristics for successful SHM for flood defences is lacking, which hampers the practical implementation. In this study, we explore the benefits of pore pressure monitoring, one of the most promising SHM techniques for earthen flood defences. The approach is based on a Bayesian pre-posterior analysis, and results are evaluated based on the Value of Information (VoI) obtained from different monitoring strategies. We specifically investigate the effect on long-term reinforcement decisions. The results show that, next to the relative magnitude of reducible uncertainty, the combination of the probability of having a useful observation and the duration of a SHM effort determine the VoI. As it is likely that increasing loads due to climate change will result in more frequent future reinforcements, the influence of scenarios of different rates of increase in future loads is also investigated. It was found that, in all considered possible scenarios, monitoring yields a positive Value of Information, hence it is an economically efficient measure for flood defence asset management both now and in the future.

Value of information of structural health monitoring with temporally dependent observations

2021 ◽  
pp. 147592172110306
Author(s):  
Jannie S Nielsen
Keyword(s):  
Structural Health Monitoring ◽  
Numerical Experiment ◽  
Bayesian Network ◽  
Health Monitoring ◽  
Value Of Information ◽  
Decision Model ◽  
Parameter Study ◽  
Maintenance Planning ◽  
Structural Health ◽  
Dependent Observations

A Bayesian approach is often applied when updating a deterioration model using observations from inspections, structural health monitoring, or condition monitoring. The observations are stochastic variables with probability distributions that depend on the damage size. Consecutive observations are usually assumed to be independent of each other, but this assumption does not always hold, especially when using online monitoring systems. Frequent updating using dependent measurements can lead to an over-optimistic assessment of the value of information when the measurements are incorrectly modeled as being independent. This article presents a Bayesian network modeling approach for the inclusion of temporal dependency between measurements through a dependency parameter and presents a generic monitoring model based on the exceedance of thresholds for a damage index. Additionally, the model is implemented in a computational framework for risk-based maintenance planning, developed for maintenance planning for wind turbines. The framework is applied for a numerical experiment, where the expected lifetime costs are found for strategies with monitoring with and without dependency between observations, and also for the case where dependency is present but is neglected when making decisions. The numerical experiment and associated parameter study show that neglecting dependency in the decision model when the observations are in fact dependent can lead to much higher costs than expected and to the selection of non-optimal strategies. Much lower costs (down to one quarter) can be obtained when the dependency is properly modeled. In the case of temporally dependent observations, an advanced decision model using a Bayesian network as a simple digital twin is needed to make monitoring feasible compared to only using inspections.

scholarly journals An alternative quantification of the value of information in structural health monitoring

2021 ◽  
pp. 147592172110284
Author(s):  
Mayank Chadha ◽  
Zhen Hu ◽  
Michael D Todd
Keyword(s):  
Structural Health Monitoring ◽  
Decision Analysis ◽  
Health Monitoring ◽  
Structural State ◽  
Value Of Information ◽  
Design Research ◽  
Cost Benefit Analysis ◽  
Information Gathering ◽  
Structural Health ◽  
System Designs

Analogous to an experiment, a structural health monitoring (SHM) system may be thought of as an information-gathering mechanism. Gathering the information that is representative of the structural state and correctly inferring its meaning helps engineers (decision-makers) mitigate possible losses by taking appropriate actions (risk-informed decision-making). However, the design, research, development, installation, maintenance, and operation of an SHM system are an expensive endeavor. Therefore, the decision to invest in new information is rationally justified if the reduction in the expected losses by utilizing newly acquired information is more than the intrinsic cost of the information acquiring mechanism incurred over the lifespan of the structure. This article investigates the economic advantage of installing an SHM system for inference of the structural state, risk, and lifecycle management by using the value of information (VoI) analysis. Among many possible choices of SHM system designs (different information-gathering mechanisms), pre-posterior decision analysis can be used to select the most feasible design. Traditionally, the cost–benefit analysis of an SHM system is carried out through pre-posterior decision analysis that helps one evaluate the benefit of an experiment or an information-gathering mechanism using the expected value of information metric. This study proposes an alternate normalized metric that evaluates the expected reward ratio (benefit/gain of using an SHM system) relative to the investment risk (cost of SHM over the lifecycle). The analysis of evaluating the relative benefit of various SHM system designs is carried out by considering the concept of the VoI, by performing pre-posterior analysis, and the idea of a perfect experiment is discussed.

scholarly journals IWSHM 2017: Quantifying the benefit of structural health monitoring: what if the manager is not the owner?

2018 ◽  
Vol 17 (6) ◽  
pp. 1393-1409 ◽  
Author(s):  
Denise Bolognani ◽  
Andrea Verzobio ◽  
Daniel Tonelli ◽  
Carlo Cappello ◽  
Branko Glisic ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
Value Of Information ◽  
Background Information ◽  
University Campus ◽  
Structural Health ◽  
Princeton University ◽  
The Difference ◽  
The Right

Only very recently our community has acknowledged that the benefit of structural health monitoring can be properly quantified using the concept of value of information ( VoI). The VoI is the difference between the utilities of operating the structure with and without the monitoring system. Typically, it is assumed that there is one decision-maker for all decisions, that is, deciding on both the investment on the monitoring system and the operation of the structure. The aim of this work is to formalize a rational method for quantifying the value of information when two different actors are involved in the decision chain: the manager, who makes decisions regarding the structure, based on monitoring data; and the owner, who chooses whether to install the monitoring system or not, before having access to these data. The two decision-makers, even if both rational and exposed to the same background information, may still act differently because of their different appetites for risk. To illustrate how this framework works, we evaluate a hypothetical VoI for the Streicker Bridge, a pedestrian bridge in Princeton University campus equipped with a fiber optic sensing system, assuming that two fictional characters, Malcolm and Ophelia, are involved: Malcolm is the manager who decides whether to keep the bridge open or close it following to an incident; Ophelia is the owner who decides whether to invest on a monitoring system to help Malcolm making the right decision. We demonstrate that when manager and owner are two different individuals, the benefit of monitoring could be greater or smaller than when all the decisions are made by the same individual. Under appropriate conditions, the monitoring VoI could even be negative, meaning that the owner is willing to pay to prevent the manager to use the monitoring system.

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