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A SI-Based Algorithm for Structural Damage Detection

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Advances in Swarm Intelligence (ICSI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7331))

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Abstract

As a challenging task in the structural health monitoring (SHM) field, structural damage detection, one of most important issues of the SHM system, is mathematically converted into a constrained optimization problem, which is then hopefully solved by a swarm intelligence (SI) based algorithm proposed in this paper. The performance of the proposed algorithm is experimentally evaluated by the measured data of four damage patterns of a building model of 3-storey steel frame structure made in laboratory. Some illustrated results show that the proposed method is very suitable for the structural multi-damage identification, which also show that the SI-based algorithm for structural damage detection can provide an effective and robust tool in the SHM field.

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Author information

Authors and Affiliations

  1. Department of Mechanics and Civil Engineering, Jinan University, Guangzhou, 510632, China

    Ling Yu, Peng Xu & Xi Chen

  2. MOE Key Lab of Disaster Forecast and Control in Engineering, Jinan University, Guangzhou, 510632, China

    Ling Yu

Authors
  1. Ling Yu
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  2. Peng Xu
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  3. Xi Chen
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Peking University, Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, Shenzhen University, 518060, Shenzhen, China

    Zhen Ji

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© 2012 Springer-Verlag Berlin Heidelberg

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Yu, L., Xu, P., Chen, X. (2012). A SI-Based Algorithm for Structural Damage Detection. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_3

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  • DOI: https://doi.org/10.1007/978-3-642-30976-2_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30975-5

  • Online ISBN: 978-3-642-30976-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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