A variational Bayesian neural network for structural health monitoring and cost-informed decision-making in miter gates

Vega, Manuel A.; Todd, Michael D.

doi:10.1177/1475921720904543

Cited by 45 publications

(28 citation statements)

References 39 publications

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“…1 shows the Greenup lock and the miter gate (image adapted from the USACE website and Eick et al [29]). Loss of contact in the quoin blocks (boundary related damage) is the most commonly observed damage mode in such systems [19,30,20]. Loss of contact leads to a formation of a gap between the gate and the wall quoin blocks at the bottom of the gate.…”

Section: Demonstration Problem Description 41 Miter Gatementioning

confidence: 99%

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

Chadha

Todd

2021

Structural Health Monitoring

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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.

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Section: Demonstration Problem Description 41 Miter Gatementioning

confidence: 99%

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

Chadha

Todd

2021

Structural Health Monitoring

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“…1 shows the Greenup lock and the miter gate (image adapted from the USACE website and Eick et al [45]). Loss of contact in the quoin blocks is the most commonly observed damage mode in such systems [39,41,40]. Loss of contact leads to a formation of a very thin gap between the gate and the wall quoin blocks at the bottom of the gate, which induces undesirable load redistribution in the system.…”

Section: Miter Gate: Finite Element Modelmentioning

confidence: 99%

“…Current practice involves engineering elicitation via inspection, followed by lock closures if the inspection so warrants. Since this process is based on the varied experience and interpretation of field inspectors, it bears high uncertainty and variability [41], and USACE is investigating the use of SHM to potentially reduce those uncertainties. In general terms, the first step of the SHM system design is to decide what sensors are most suitable (e.g., discrete or continuous strain-gauges [42], accelerometers, etc.)…”

Section: Introductionmentioning

confidence: 99%

A probabilistic optimal sensor design approach for structural health monitoring using risk-weightedf-divergence

Yang

Chadha

et al. 2021

Mechanical Systems and Signal Processing

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This paper presents a new approach to optimal sensor design for structural health monitoring (SHM) applications using a modified -divergence objective functional. One of the primary goals of SHM is to infer the unknown and uncertain damage state parameter(s) from the acquired data or features derived from the data. In this work, we consider the loss of boundary contact (a "gap") between a navigation lock miter gate and the supporting wall quoin block at the bottom of the gate to be the damage state parameter of concern. The design problem requires the optimal sensor placement of strain gages to obtain the best possible inference of the probability distribution of the gap length using the data from the multi-dimensional strain-gauge array. Using the notion of -divergences (measures of difference between probability distributions), a risk-adjustment is made by using functions that weigh the importance of acquiring useful information for a given true value of the state-parameter and using Bayesian optimization. For this case study of miter gate monitoring, a computationally expensive high-fidelity finite element model and its digital surrogate is employed to provide efficient, previously-validated data.

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“…To fulfill this purpose, the concept of structural health monitoring (SHM) was proposed, and there has been great development in the SHM area utilizing dynamic measurements in the last few decades 1 . A large amount of methods has been proposed in this research area and applied to various types of basic structural members, laboratory models, and even real‐life structures, including trusses, 2–4 beams and frames, 5–11 plates and shells, 12–18 periodic structures, 19–23 hydraulic steel structures, 24–26 buildings, 27–33 and bridges 34–40 . However, in addition to the specific methods employed for solving the relevant problems as mentioned above, the success of vibration‐based SHM also highly depends on the quality of collected measurement data, which is ensured by the quantity and detailed layout of employed sensors; at present, the sensor configuration is still designed based on experience in most cases by considering a series of practical constraints.…”

Section: Introductionmentioning

confidence: 99%

Optimal sensor configuration for structural response prediction by a modified Nelder–Mead simplex method

Yin

2021

Struct Control Health Monit

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Summary The optimal configuration of the sensor system is extremely critical to effectively control the test cost and ensure the quality of the collected data for structural health monitoring (SHM) system. Different from the traditional sensor configuration criterion, the information entropy measure aims to minimize the uncertainty of structural model parameters to be identified, which can ensure that the collected data provides the maximum amount of information for the model identification. However, most of the current researches within the framework of entropy measure are aimed at the model identification, while studies that consider response prediction are still rare. In this paper, by constructing the posterior predictive distribution function of the frequency response functions (FRFs) at the unmeasured positions of structures and also the corresponding information entropy representation, the optimal sensor configuration problem for the purpose of structural response prediction at the unmeasured position is studied. A modified bound‐constrained Nelder–Mead simplex method to handle the binary encoded optimization variables is also proposed, which can effectively solve the combinatorial optimization problem in the optimal sensor configuration. The methodology proposed is validated by a forced vibration study conducted for a laboratory rectangular cantilever plate. The obtained results show that the prediction error and uncertainty of the higher‐order modes of the FRFs at the unmeasured positions is generally larger than that of the lower ones. Also, by comparing the performance of worst and optimal sensor layouts, the importance of the optimal configuration for response prediction at unmeasured locations is clearly revealed especially for a small number of sensors.

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A variational Bayesian neural network for structural health monitoring and cost-informed decision-making in miter gates

Cited by 45 publications

References 39 publications

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

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

A probabilistic optimal sensor design approach for structural health monitoring using risk-weightedf-divergence

Optimal sensor configuration for structural response prediction by a modified Nelder–Mead simplex method

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