Neural networks-based damage detection for bridges considering errors in baseline finite element models

Lee, Jong-Jae; Lee, Jong Won; Yi, Jin‐Hak; Yun, Chung Bang; Jung, Hie Young

doi:10.1016/j.jsv.2004.01.003

Cited by 207 publications

(115 citation statements)

References 31 publications

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“…Mode shape-based methods traditionally use differences in mode shapes between healthy and damaged structures as the basic feature for damage detection [9,10]. Compared to natural frequency-based methods, mode shape-based methods have the significant advantage of containing information that makes them more sensitive to local damages.…”

Section: Introductionmentioning

confidence: 99%

An investigation into the acceleration response of a damaged beam-type structure to a moving force

González

Hester

2013

Journal of Sound and Vibration

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Publication information Journal of Sound and Vibration, 332 (13): 3201-3217Publisher Elsevier Item record/more information http://hdl.handle.net/10197/6212 Publisher's statementThis is the author's version of a work that was accepted for publication in Journal of Sound and Vibration. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Sound and Vibration (VOL 332, ISSUE 13, (2013) that did not appear in the healthy structure is present in the response of the damaged structure. This paper elucidates from first principles how the acceleration response can be assumed to consist of 'static' and 'dynamic' components, and where the beam has experienced a localised loss in stiffness, an additional 'damage' component. The combination of these components establishes how the damage singularity will appear in the total response.For a given damage severity, the amplitude of the 'damage' component will depend on how close the damage location is to the sensor, and its frequency content will increase with higher velocities of the moving force. The latter has implications for damage detection because if the frequency content of the 'damage' component includes bridge and/or vehicle frequencies, it becomes more difficult to identify damage. The paper illustrates how a thorough understanding of the relationship between the 'static' and 'damage' components contributes *Manuscript Click here to download Manuscript: Manuscript RevC.doc Click here to view linked References 2 to establish if damage has occurred and to provide an estimation of its location and severity.The findings are corroborated using accelerations from a planar finite element simulation model where the effects of force velocity and bridge span are examined.

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Section: Introductionmentioning

confidence: 99%

An investigation into the acceleration response of a damaged beam-type structure to a moving force

González

Hester

2013

Journal of Sound and Vibration

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“…Lee et al [31] presented a neural networkbased technique for element-level damage assessments of structures using the modal shape differences or ratios of intact and damaged structures. The effectiveness and applicability of the proposed method using the mode shape differences or ratios were demonstrated by two numerical example analyses on a simple beam and a multi-girder bridge.…”

Section: Traditionalmentioning

confidence: 99%

Vibration-based Damage Identification Methods: A Review and Comparative Study

Fan

Qiao

2010

Structural Health Monitoring

1,633

215

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A comprehensive review on modal parameter-based damage identification methods for beam-or plate-type structures is presented, and the damage identification algorithms in terms of signal processing are particularly emphasized. Based on the vibration features, the damage identification methods are classified into four major categories: natural frequency-based methods, mode shape-based methods, curvature mode shape-based methods, and methods using both mode shapes and frequencies, and their merits and drawbacks are discussed. It is observed that most mode shape-based and curvature mode shape-based methods only focus on damage localization. In order to precisely locate the damage, the mode shape-based methods have to rely on optimization algorithms or signal processing techniques; while the curvature mode shape-based methods are in general a very effective type of damage localization algorithms. As an implementation, a comparative study of five extensively-used damage detection algorithms for beam-type structures is conducted to evaluate and demonstrate the validity and effectiveness of the signal processing algorithms. This brief review aims to help the readers in identifying starting points for research in vibration-based damage identification and structural health monitoring and guides researchers and practitioners in better implementing available damage identification algorithms and signal processing methods for beam-or plate-type structures.

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“…Meanwhile, they vary less sensitively to environmental effects. Modal perturbation analysis indicates that modal shape ratios are also less sensitive to the modeling errors than frequency [17].…”

Section: Introductionmentioning

confidence: 99%

A fuzzy logic-based damage identification method for simply-supported bridge using modal shape ratios

Liu¹,

Jiao²,

Gong³

2012

IJCIS

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A fuzzy logic system (FLS) is established for damage identification of simply supported bridge. A novel damage indicator is developed based on ratios of mode shape components between before and after damage. Numerical simulation of a simply-supported bridge is presented to demonstrate the memory, inference and anti-noise ability of the proposed method. The bridge is divided into eight elements and nine nodes, the damage indicator vector at characteristic nodes is used as the input measurement of FLS. Results reveal that FLS can detect damage of training patterns with an accuracy of 100%. Aiming at other test patterns, the FLS also possesses favorable inference ability, the identification accuracy for single damage location is up to 93.75%. Tests with noise simulated data show that the FLS possesses favorable anti-noise ability.

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Neural networks-based damage detection for bridges considering errors in baseline finite element models

Cited by 207 publications

References 31 publications

An investigation into the acceleration response of a damaged beam-type structure to a moving force

An investigation into the acceleration response of a damaged beam-type structure to a moving force

Vibration-based Damage Identification Methods: A Review and Comparative Study

A fuzzy logic-based damage identification method for simply-supported bridge using modal shape ratios

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