Bayesian Optimal Sensor Placement for Modal Identification of Civil Infrastructures

Argyris, Costas; Papadimitriou, Costas; Panetsos, Panagiotis

doi:10.18063/jsc.2016.02.001

Cited by 25 publications

(22 citation statements)

References 16 publications

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“…On the other hand, hybrid approaches may exploit the advantages of both the model‐based and data‐driven path. Finally, it should be noted that the issue of detection and localization should be combined with optimal sensor placement methodologies or ensuring and enhancing detectability.…”

Section: Recent Progress On Damage Identification Methods For Beam Brmentioning

confidence: 99%

Recent progress and future trends on damage identification methods for bridge structures

Chatzi

Sim

et al. 2019

Struct Control Health Monit

231

112

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Summary Damage identification forms a key objective in structural health monitoring. Several state‐of‐the‐art review papers regarding progress in this field up to 2011 have been published. This paper summarizes the recent progress between 2011 and 2017 in the area of damage identification methods for bridge structures. This paper is organized based on the classification of bridge infrastructure in terms of fundamental structural systems, namely, beam bridges, truss bridges, arch bridges, cable‐stayed bridges, and suspension bridges. The overview includes theoretical developments, enhanced simulation attempts, laboratory‐scale implementations, full‐scale validation, and the summary for each type of bridges. Based on the offered review, some challenges, suggestions, and future trends in damage identification are proposed. The work can be served as a basis for both academics and practitioners, who seek to implement damage identification methods in next‐generation structural health monitoring systems.

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Section: Recent Progress On Damage Identification Methods For Beam Brmentioning

confidence: 99%

Recent progress and future trends on damage identification methods for bridge structures

Chatzi

Sim

et al. 2019

Struct Control Health Monit

231

112

View full text Add to dashboard Cite

show abstract

“…Typically, the importance of parameters to model responses have been averaged (Matos et al 2016) or an intersection of parameters important to response at all sensor locations (Van Buren et al 2015) have been assumed to provide an optimal model class. However, novel sensor placement strategies (Papadopoulou et al 2015;Bertola et al 2017;Argyris et al 2017) have been developed with the aim of minimizing the number of sensors and maximizing information from each sensor.…”

Section: Introductionmentioning

confidence: 99%

Model-Class Selection Using Clustering and Classification for Structural Identification and Prediction

Pai¹,

Sanayei

Smith

2021

J. Comput. Civ. Eng.

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Structural identification using physics-based models and subsequent prediction have much potential to enhance civil infrastructure asset-management decision-making. Interpreting monitoring information in the presence of multiple uncertainty sources and systematic bias using a physics-based model is a computationally expensive task. The computational cost of this task is exponentially proportional to the number of model parameters updated using monitoring data. In this paper, a novel model-class selection 1 method is proposed to obtain computationally optimal and identifiable model classes. Unlike traditional sensitivity methods for model-class selection, in the proposed method, model responses at sensor locations are clustered to identify underlying trends in model response datasets. K-means clustering is used to determine relevant clusters in the data. Cluster indices are then used as labels for classification. Support-vector machine classification using forward variable selection with sequential search is used to select model parameters that help classify trends in data. The result of the sequential search is a trade-off curve comparing classification error with number of parameters in the model class. This curve helps select a practical and near optimal model class. The modelclass selection method proposed in this paper is compared with linear regression-based sensitivity analysis using a full-scale bridge. Identification with model classes obtained using both methods for two sensor configurations suggests that the model-based clustering method helps select an identifiable and computationally efficient model class. The minimum remaining fatigue life of the bridge predicted using the updated model classes is 720 years and this represents fatigue-life extension of ten times compared with design predictions prior to measurements. This approach provides good support for asset managers when they interpret measurement data.

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“…Also, the comparison between sensor configurations involving different numbers of sensors is allowed, and this is particularly beneficial for trading off the cost of instrumentation with information gained from additional sensors. This methodology is also employed for investigating the optimal configuration of a given number of sensors for the target structures with a relatively large amount of degrees of freedom (DOFs), including the transmission tower, 52 lattice tower model, 53 and real‐life buildings 54 and bridges 47,55 …”

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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Bayesian Optimal Sensor Placement for Modal Identification of Civil Infrastructures

Cited by 25 publications

References 16 publications

Recent progress and future trends on damage identification methods for bridge structures

Recent progress and future trends on damage identification methods for bridge structures

Model-Class Selection Using Clustering and Classification for Structural Identification and Prediction

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

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