Structural identification and damage detection using genetic algorithms
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Structural identification and damage detection using genetic algorithms by Chan Ghee Koh

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Published by CRC Press in Boca Raton .
Written in English

Subjects:

  • Structural analysis (Engineering) -- Mathematics,
  • Fault location (Engineering) -- Mathematics,
  • Genetic algorithms

Book details:

Edition Notes

Includes bibliographical references and index.

StatementC.G. Koh and M.J. Perry.
SeriesStructures and infrastructures series -- v. 6
ContributionsPerry, M. J. 1981-
Classifications
LC ClassificationsTA646 .K56 2010
The Physical Object
Paginationp. cm.
ID Numbers
Open LibraryOL23909080M
ISBN 109780415461023, 9780415876292
LC Control Number2009038174

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This book is intended for researchers, engineers and graduate students in structural and mechanical engineering, particularly for those interested in model calibration, parameter estimation and damage detection of structural and mechanical systems using the state-of-the-art GA methodology. Get this from a library! Structural identification and damage detection using genetic algorithms. [Chan Ghee Koh; M J Perry] -- "Rapid advances in computational methods and computer capabilities have led to a new generation of structural identification strategies. Robust and efficient methods have successfully been developed. Structural Identification and Damage Detection using Genetic Algorithms: Structures and Infrastructures Book Series, Vol. 6 - CRC Press Book Rapid advances in computational methods and computer capabilities have led to a new generation of structural identification strategies. Structural Identification and Damage Detection 3 Overview of Structural Identification Methods 3 2. A Primer to Genetic Algorithms 15 Background to GA 15 A Simple GA 17 Theoretical Framework 22 Advances in GAs 24 Chapter Summary 27 3. An Improved GA Strategy 29 SSRM 30 iGAMAS 34 Chapter Summary 43 4.

In this chapter, the latest developments by the authors in the area of structural identification and structural damage detection using genetic algorithms are : C. G. Koh. Genetic algorithms explore the region of the whole solution space and can obtain the global optimum. In this paper, a genetic algorithm with real number encoding is applied to identify the structural damage by minimizing the objective function, which directly compares . Structural damage detection consists of determining the location and severity of damage in civil structures by using measured parameters. Genetic algorithm is a meta-heuristic computing method for finding approximated global minimums in large optimization problems and has the advantage of Cited by: 7. Genetic algorithms (GA) have proved to be a robust, efficient search technique for many problems. In this chapter, the latest developments by the authors in the area of structural identification and structural damage detection using genetic algorithms are presented. A GA strategy involving a search Cited by: 2.

  This book is intended for researchers, engineers and graduate students in structural and mechanical engineering, particularly for those interested in model calibration, parameter estimation and damage detection of structural and mechanical systems using the state-of-the-art GA methodology. It is the intention of this book, believed to be the first on this topic, to provide readers with the background and recent developments on GA-based methods for parameter identification, model updating and damage detection of structural dynamic systems.   Structural damage identification based on finite element (FE) model updating has been a research direction of increasing interest over the last decade in the mechanical, civil, aerospace, etc., engineering fields. Various studies have addressed direct, sensitivity-based, probabilistic, statistical, and iterative methods for updating FE models for structural damage by:   The damage identification method developed using the implicit redundant genetic algorithm provides greater accuracy in identifying the location and severity of damage in all case studies even in the presence of noise.