A Bayesian approach based on a Markov-chain Monte Carlo method for damage detection under unknown sources of variability

Figueiredo, Elói; Radu, Lucian; Worden, Keith; Farrar, Charles R.

doi:10.1016/j.engstruct.2014.08.042

Cited by 83 publications

(52 citation statements)

References 12 publications

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“…Papadimitriou and Papadioti introduced a component mode synthesis technique in a Bayesian FE model‐updating framework, for solving the forward analysis problem in a reduced space of generalized coordinates; the technique is demonstrated for damage identification applications on the Metsovo highway bridge (Greece) using simulated measurements. Figueiredo et al proposed a Bayesian approach for bridge damage detection based on the Markov chain Monte Carlo method with unknown sources of variability; the algorithm can detect structural damage using daily response data, even in the case of abnormal events such as the temperature variation; the proposed method was applied to damage detection of the Z‐24 bridge, and results show the proposed method has good robustness for damage detection. Ma et al proposed a new framework for predicting the remaining strength of bridges based on a Bayesian network and in situ load testing; the Bayesian network is developed to predict structural strength degradation under the influence of stiffness degradation, corrosion damage, load‐deflection response, and other factors; theoretical and experimental results of an existing reinforced concrete bridge show that the proposed method can improve the prediction accuracy.…”

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

216

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

216

112

View full text Add to dashboard Cite

show abstract

“…This is because minor damage does not cause large changes in structural dynamic responses. For long-term SHM, previous researches concentrated on the outlier detection by using several models such as statistical models, discriminant analysis and so on [6][7][8], while few works paid attention to the efficiency, especially in transmissibility based SHM.…”

Section: Introductionmentioning

confidence: 99%

Rapid early damage detection using transmissibility with distance measure analysis under unknown excitation in long-term health monitoring

Zhou¹,

Wahab²

2016

J. vibroeng.

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Accurate and efficient damage detection in long-term health monitoring for structures still encounters many difficulties due to the effect of environment. Furthermore, recorded big data requires efficient damage detection algorithm. In this study, an efficient and effective damage detection algorithm is proposed using transmissibility along with Mahalanobis distance and Hotelling T-square. A numerically simulated beam and an experimentally tested laboratory structure are used to validate the proposed algorithm. Results demonstrate good performance of the proposed technique in damage detection.

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“…As for vibration-based SHM, the core idea is to find a sensitive feature able to discriminate a damaged structure when compared to the baseline structure (healthy state); therefore the damage detection conclusion can be drawn out with choosing a threshold of the change, before and after damage, considering the influence of operational variety. As described in the introduction, lots of approaches, features, and measurement methodologies have been used in the past [2,3,26,27].…”

Section: Transmissibility Based Coherencementioning

confidence: 99%

Damage Detection and Quantification Using Transmissibility Coherence Analysis

Zhou

Figueiredo

Maia

et al. 2015

Shock and Vibration

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A new transmissibility-based damage detection and quantification approach is proposed. Based on the operational modal analysis, the transmissibility is extracted from system responses and transmissibility coherence is defined and analyzed. Afterwards, a sensitive-damage indicator is defined in order to detect and identify the severity of damage and compared with an indicator developed by other authors. The proposed approach is validated on data from a physics-based numerical model as well as experimental data from a three-story aluminum frame structure. For both numerical simulation and experiment the results of the new indicator reveal a better performance than coherence measure proposed in Rizos et al., 2008, Rizos et al., 2002, Fassois and Sakellariou, 2007, especially when nonlinearity occurs, which might be further used in real engineering. The main contribution of this study is the construction of the relation between transmissibility coherence and frequency response function coherence and the construction of an effective indicator based on the transmissibility modal assurance criteria for damage (especially for minor nonlinearity) detection as well as quantification.

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A Bayesian approach based on a Markov-chain Monte Carlo method for damage detection under unknown sources of variability

Cited by 83 publications

References 12 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

Rapid early damage detection using transmissibility with distance measure analysis under unknown excitation in long-term health monitoring

Damage Detection and Quantification Using Transmissibility Coherence Analysis

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