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

Kamariotis, Antonios; Chatzi, Eleni; Straub, Daniel

doi:10.1016/j.ymssp.2022.109708

Cited by 71 publications

(28 citation statements)

References 44 publications

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“…However, use of off-line methods for on-line estimation tasks is computationally prohibitive (Del Moral et al, 2006; Kantas et al, 2015). Additionally, when considering off-line inference, in settings when measurements are obtained sequentially at different points in time, off-line MCMC methods tend to induce a larger computational cost than on-line particle filter methods, which can be important, for example, when optimizing inspection and monitoring (Papakonstantinou and Shinozuka, 2014; Luque and Straub, 2019; Kamariotis et al, 2023). Questions that we investigate in this context include: Can one precisely quantify the posterior uncertainty of time-invariant parameters when employing on-line particle filter methods?…”

Section: Introductionmentioning

confidence: 99%

On off-line and on-line Bayesian filtering for uncertainty quantification of structural deterioration

Kamariotis

Sardi

Papaioannou

et al. 2023

DCE

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Data-informed predictive maintenance planning largely relies on stochastic deterioration models. Monitoring information can be utilized to update sequentially the knowledge on model parameters. In this context, on-line (recursive) Bayesian filtering algorithms typically fail to properly quantify the full posterior uncertainty of time-invariant model parameters. Off-line (batch) algorithms are—in principle—better suited for the uncertainty quantification task, yet they are computationally prohibitive in sequential settings. In this work, we adapt and investigate selected Bayesian filters for parameter estimation: an on-line particle filter, an on-line iterated batch importance sampling filter, which performs Markov Chain Monte Carlo (MCMC) move steps, and an off-line MCMC-based sequential Monte Carlo filter. A Gaussian mixture model approximates the posterior distribution within the resampling process in all three filters. Two numerical examples provide the basis for a comparative assessment. The first example considers a low-dimensional, nonlinear, non-Gaussian probabilistic fatigue crack growth model that is updated with sequential monitoring measurements. The second high-dimensional, linear, Gaussian example employs a random field to model corrosion deterioration across a beam, which is updated with sequential sensor measurements. The numerical investigations provide insights into the performance of off-line and on-line filters in terms of the accuracy of posterior estimates and the computational cost, when applied to problems of different nature, increasing dimensionality and varying sensor information amount. Importantly, they show that a tailored implementation of the on-line particle filter proves competitive with the computationally demanding MCMC-based filters. Suggestions on the choice of the appropriate method in function of problem characteristics are provided.

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Section: Introductionmentioning

confidence: 99%

On off-line and on-line Bayesian filtering for uncertainty quantification of structural deterioration

Kamariotis

Sardi

Papaioannou

et al. 2023

DCE

Self Cite

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“…The use of SHM technologies for bridge assets management poses challenge due to the complexities involved in accurately determining the cost-benefit ratio of monitoring activities. Over the past decade, several probabilisticbased techniques have emerged for numerically quantifying the impact of SHM technologies in a life-cycle cost framework (LCCA) [1][2]. The LCCA allows to compute lifetime cost of each structure considering the whole possible cost items from construction to disposal.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, slowly-evolving deterioration phenomena over bridge's lifetime should be considered to obtain informative data to implement a time-dependent reliability analysis [9]. In the present-day practice, visual inspections are the primary, and often only, means of obtaining information regarding the condition of a bridge throughout its life cycle [2]. Indeed, in the Italian Guidelines on Risk Classification and Management of Bridges [10] the role of visual surveys is highlighted and the use of SHM is encouraged for those bridges whose preliminary risk estimations (the so-called "attention classes") are classified as high or medium-high.…”

Section: Introductionmentioning

confidence: 99%

A LCCA framework for the management of bridges based on data fusion from visual inspections and SHM systems.

Ierimonti,

Mariani,

Venanzi

et al. 2023

ce papers

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Bridges' integrity, safety and serviceability are fundamental targets that need to be ensured during the structural lifetime, owing to the crucial role that such infrastructure components play in modern communities. Recently, a particular attention is being paid to the management of bridges since many of them have reached the end of their expected service life. To this purpose, the Italian Ministry of Sustainable Mobility issued in 2020 the “Guidelines for risk classification and management, safety assessment and monitoring of bridges” that regulate visual inspections, risk assessment based on multilevel protocols and consequent monitoring and maintenance activities. In this context, structural health monitoring (SHM) systems play a fundamental role to guarantee safety and adequate performance levels. However, the synergy between visual inspections and SHM is yet to be fully understood and exploited. The main goal of this research work is to contribute to filling this gap by proposing a general probabilistic framework, based on Life‐Cycle Cost Analysis (LCCA), able to fuse information from SHM data and visual inspections, with the aim to plan over time short‐term and long‐term interventions and to optimize fund allocation by considering all the stored information.

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“…The VoI represents the expected reduction in management costs associated with the acquisition of new information [6][7][8]. This concept has been applied in various areas such as emergency management [9,10], sensor deployment optimization [11,12], and maintenance strategy definition [13,14]. Generally, VoI analyses are carried out under the assumption that the SHM system is always working correctly.…”

Section: Introductionmentioning

confidence: 99%

Value of information analysis accounting for data quality

Giordano

Quqa

Limongelli

2023

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023

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Structural Health Monitoring (SHM) can provide valuable information for maintenance-related activities and post-disaster emergency management. However, as with any technological system, SHM systems can be susceptible to errors due to malfunctioning. Therefore, it is essential to assess the potential for malfunctions and the associated costs of maintenance and repair when evaluating the long-term benefits of SHM systems. In the last two decades, sensor validation tools (SVTs) have been proposed to support decisions by isolating and discarding abnormal data. Recently, the authors of this paper have proposed a framework based on the Value of Information (VoI) from Bayesian decision analysis to account for different states of an SHM system and assess the benefit of SVT information. By quantifying the additional value obtained from SVTs, decision-makers can make more informed decisions about investing in these systems. This framework is here demonstrated on a real case study, namely the S101 bridge in Austria, which has been artificially damaged for research purposes. The benefit of collecting SHM and SVT information is quantified by considering a simple decision problem related to the management of the bridge in the aftermath of a damaging event. Overall, the study highlights the potential benefits of using SVTs to improve the reliability of SHM data and inform decision-making in the management of structures.

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

Cited by 71 publications

References 44 publications

On off-line and on-line Bayesian filtering for uncertainty quantification of structural deterioration

On off-line and on-line Bayesian filtering for uncertainty quantification of structural deterioration

A LCCA framework for the management of bridges based on data fusion from visual inspections and SHM systems.

Value of information analysis accounting for data quality

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