A robust singular value decomposition for damage detection under changing operating conditions and structural uncertainties

Vanlanduit, Steve; Parloo, Eli; Cauberghe, Bart; Guillaume, Patrick; Verboven, Peter

doi:10.1016/j.jsv.2004.07.016

Cited by 82 publications

(56 citation statements)

References 6 publications

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“…A comparison has been made by Yan et al (2004) between the work of Peeters and De Roeck (2000) and the one developed by Yan et al (2005b), leading to the argument that by using the PCA-based damage detection method the problem becomes simpler, because only vibration features are needed to determine if the structure is damaged. Vanlanduit et al (2005) introduce the robust singular value decomposition (RSVD) for damage detection from measurements taken under different operational conditions, i.e., different excitation levels, geometrical uncertainties and surface treatments. This method is based on the SVD of a matrix…”

Section: More Recently Santos Et Al (2004) (2005) Presented a Damagmentioning

confidence: 99%

A Review of Vibration-based Structural Health Monitoring with Special Emphasis on Composite Materials

Montalvão¹,

Maia²,

Ribeiro³

2006

The Shock and Vibration Digest

419

243

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Structural health monitoring and damage detection techniques are tools of great importance namely in the offshore, civil, mechanical and aeronautical engineering communities, either due to safety reasons or to the economical benefits it may bring. The need for detecting damage in complex structures has led to the development of a vast amount of techniques, in particular those based upon structural vibration analysis. In the present article, some of the latest advances in Structural Health Monitoring and Damage Detection have been covered, with an emphasis on composite structures, given the fact that this kind of materials have currently a wide range of engineering applications.

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Section: More Recently Santos Et Al (2004) (2005) Presented a Damagmentioning

confidence: 99%

A Review of Vibration-based Structural Health Monitoring with Special Emphasis on Composite Materials

Montalvão¹,

Maia²,

Ribeiro³

2006

The Shock and Vibration Digest

419

243

View full text Add to dashboard Cite

show abstract

“…In this way, the LTS is less sensitive to local variations and dubious data. Vanlanduit et al [100] also employed the same technique to increase their regression model's robustness when used in conjunction with Single Value Decomposition (SVD) for damage detection. Their subsequent comparison with standard linear regression techniques showed that the inclusion of a screening stage such as the C-step improves the accuracy of the model and subsequent damage detection.…”

Section: Regression Modelsmentioning

confidence: 99%

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

2017

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Abstract:Traditionally, damage identification techniques in bridges have focused on monitoring changes to modal-based Damage Sensitive Features (DSFs) due to their direct relationship with structural stiffness and their spatial information content. However, their progression to real-world applications has not been without its challenges and shortcomings, mainly stemming from: (1) environmental and operational variations; (2) inefficient utilization of machine learning algorithms for damage detection; and (3) a general over-reliance on modal-based DSFs alone. The present paper provides an in-depth review of the development of modal-based DSFs and a synopsis of the challenges they face. The paper then sets out to addresses the highlighted challenges in terms of published advancements and alternatives from recent literature.

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“…Conventional approaches include regression analysis [17], novelty detection [12], missing data analysis [8,9], singular value decomposition [19,22], and a support vector machine [15]. Another approach evaluating the environmental effects statistically with measured signals only without using an analytical model includes principal component analysis(PCA) [25], factor analysis [5,7] and neural network(NN) [6].…”

Section: Elimination Of Environmental Effectsmentioning

confidence: 99%

Bridge Health Monitoring with Consideration of Environmental Effects

Kim¹,

Kim²,

Shin

et al. 2012

Journal of the Korean Society for Nondestructive Testing

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Reliable response measurements are extremely important for proper bridge health monitoring but incomplete and unreliable data may be acquired due to sensor problems and environmental effects. In the case of a sensor malfunction, parts of the measured data can be missing so that the structural health condition cannot be monitored reliably. This means that the dynamic characteristics of natural frequencies can change as if the structure is damaged due to environmental effects, such as temperature variations. To overcome these problems, this paper proposes a systematic procedure of data analysis to recover missing data and eliminate the environmental effects from the measured data. It also proposes a health index calculated statistically using revised data to evaluate the health condition of a bridge. The proposed method was examined using numerically simulated data with a truss structure and then applied to a set of field data measured from a cable-stayed bridge.

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A robust singular value decomposition for damage detection under changing operating conditions and structural uncertainties

Cited by 82 publications

References 6 publications

A Review of Vibration-based Structural Health Monitoring with Special Emphasis on Composite Materials

A Review of Vibration-based Structural Health Monitoring with Special Emphasis on Composite Materials

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

Bridge Health Monitoring with Consideration of Environmental Effects

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