The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions

Chadha, Mayank; Ramancha, Mukesh K.; Vega, Manuel A.; Conte, Joseph Le; Todd, Michael D.

doi:10.1016/j.ress.2022.108845

Cited by 20 publications

(6 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study by Valkonen and Glisic [145] creates a risk appetite assessment tool based on a domain-specific risk-taking scale to assess differences in stakeholders' attitudes toward risk, since such differences may make the negative value of the SHM system [146]. Based on the description and quantification of human preferences in the above studies, Chadha et al [147] proposed a methodology to accommodate and mitigate the risk associated with various factors. This involves considering the risk profile of the decision maker's behavior, combined with the decision maker's acceptable risk intensity.…”

Section: The Design Of Decision-making Support Frameworkmentioning

confidence: 99%

Literature Review on the Structural Health Monitoring (SHM) of Sustainable Civil Infrastructure: An Analysis of Influencing Factors in the Implementation

Wang,

2024

Buildings

View full text Add to dashboard Cite

Structural health monitoring (SHM) of civil infrastructure is significant for sustainable development. This review aims to identify the factors influencing sustainable civil infrastructure SHM implementation and analyze the properties, paths, and conditions under which they have an impact. The method adopted is a coding study based on Grounded Theory. First, the SHM implementation process in the literature is categorized through open coding to obtain an implementation framework that contains phase and activity levels. Second, based on this framework, a synthesis is conducted to categorize the influencing factors in dimensions of contents and properties through open coding and axial coding. Finally, selective coding is used to extract the factors that interacted across activities to propose a scheme of influencing factor relationships. The main findings of the synthesis are shown as follows: (1) sensor work scheduling and data transmission are promising endeavors to balance economic and environmental sustainability, while social sustainability is mainly in terms of safety and user experience; (2) the success of sustainable civil infrastructure SHM requires a collaborative technical and organizational effort; (3) since the influencing factors at different phases may interact with each other, the implementation process should emphasize forward-looking and holistic thinking.

show abstract

Section: The Design Of Decision-making Support Frameworkmentioning

confidence: 99%

Literature Review on the Structural Health Monitoring (SHM) of Sustainable Civil Infrastructure: An Analysis of Influencing Factors in the Implementation

Wang,

2024

Buildings

View full text Add to dashboard Cite

show abstract

“…Selecting the appropriate form for the utility function can be interpreted as a reflection of the decision-maker's risk profile, as discussed in Chadha et al [28]. For the purposes of this work, a risk-neutral approach is followed which translates to a linear utility function.…”

Section: Principles Of Bayesian Decision Theorymentioning

confidence: 99%

“…It should be noted that decision-theoretic concepts are being used by the SHM community. Primarily it has been employed for SHM-aided maintenance planning either in the form of posterior decision analysis [27,28], or in a pre-posterior analysis context where quantities such as the value of information (VoI) or value of SHM (VoSHM) are used to justify investment in an SHM system [29] or for optimal sensor placement (OSP) [30]. Additionally, Hughes et al [31,32] have employed decisiontheoretic principles to propose risk-based active learning, an online strategy where data labels in classification tasks are queried according to the decision support context in which these classifiers are employed.…”

Section: Introductionmentioning

confidence: 99%

On the Detection of Thickness Loss in Ship Hull Structures Through Strain Sensing

Silionis¹,

Anyfantis²

2022

Lecture Notes in Civil Engineering

View full text Add to dashboard Cite

Fatigue crack growth is one of the most common types of deterioration in metal structures with significant implications on their reliability. Recent advances in Structural Health Monitoring (SHM) have motivated the use of structural response data to predict future crack growth under uncertainty, in order to enable a transition towards predictive maintenance. Accurately representing different sources of uncertainty in stochastic crack growth (SCG) processes is a non-trivial task. The present work builds on previous research on physics-based SCG modeling under both material and loadrelated uncertainty. The aim here is to construct computationally efficient, probabilistic surrogate models for SCG processes that successfully encode these different sources of uncertainty. An approach inspired by latent variable modeling is employed that utilizes Gaussian Process (GP) regression models to enable the surrogates to be used to generate prior distributions for different Bayesian SHM tasks as the application of interest. Implementation is carried out in a numerical setting and model performance is assessed for two fundamental crack SHM problems; namely crack length monitoring (damage quantification) and crack growth monitoring (damage prognosis).

show abstract

“…SHM can be regarded as a collection of methodologies [ 1 ], sensors [ 2 , 3 , 4 ], and algorithms [ 5 , 6 , 7 , 8 ] that aim to automatically and continuously assess the structural integrity of high-value structures and systems [ 9 , 10 ]. The objective is to lessen and ultimately avoid costly and frequent maintenance inspections on safety-critical assets such as bridges, aircrafts, and vessels, effectively reducing their life cycle cost, increasing their operational lifespan, and improving their overall safety [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Variable Thickness Strain Pre-Extrapolation for the Inverse Finite Element Method

Poloni

Oboe

Sbarufatti

et al. 2023

Sensors

View full text Add to dashboard Cite

The inverse Finite Element Method (iFEM) has recently gained much popularity within the Structural Health Monitoring (SHM) field since, given sparse strain measurements, it reconstructs the displacement field of any beam or shell structure independently of the external loading conditions and of the material properties. However, in principle, the iFEM requires a triaxial strain measurement for each inverse finite element, which is seldom feasible in practical applications due to both costs and cabling-related limitations. To alleviate this problem several techniques to pre-extrapolate the measured strains have been developed, so that interpolated or extrapolated strain values are inputted to elements without physical sensors: the benefit is that the required number of sensors can be reduced. Nevertheless, whenever the monitored components comprise regions of different thicknesses, each region of constant thickness must be extrapolated separately, due to thickness-induced discontinuities in the strain field. This is the case in many practical applications, especially those concerning fiber-reinforced composite laminates. This paper proposes to extrapolate the measured strain field in a thickness-normalized space, where the thickness-induced trends are removed; this novel method can significantly decrease the number of required sensors, effectively reducing the costs of iFEM-based SHM systems. The method is validated in a simple but informative numerical case study, highlighting the potentialities and benefits of the proposed approach for more complex application scenarios.

show abstract

The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions

Cited by 20 publications

References 61 publications

Literature Review on the Structural Health Monitoring (SHM) of Sustainable Civil Infrastructure: An Analysis of Influencing Factors in the Implementation

Literature Review on the Structural Health Monitoring (SHM) of Sustainable Civil Infrastructure: An Analysis of Influencing Factors in the Implementation

On the Detection of Thickness Loss in Ship Hull Structures Through Strain Sensing

Variable Thickness Strain Pre-Extrapolation for the Inverse Finite Element Method

Contact Info

Product

Resources

About