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Machine Learning Techniques for Structural Health Monitoring Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Damage Assessment in Beam-Like Structures Using Cuckoo Search Algorithm and Experimentally Measured Data

Authors : H. Tran-Ngoc, S. Khatir, G. De Roeck, T. Bui-Tien, M. Abdel Wahab

Published in: Proceedings of the 13th International Conference on Damage Assessment of Structures

Publisher: Springer Singapore

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Abstract

This paper presents an approach for damage identification in a steel structure using Cuckoo Search (CS) algorithm. CS is an evolutionary algorithm based on global search techniques, which provides a higher opportunity for seeking the best solution and avoid local minima. A steel beam calibrated on experimental modal analysis is applied to assess the efficiency of the proposed algorithm. While a finite element (FE) model is created using MATLAB to estimate structural dynamic behavior, measurement is carried out using excitation sources of a hammer. Dynamic characteristics are selected as an objective function to minimize the discrepancy between the results of numerical model and measurements. The results show that the proposed algorithm can accurately identify damage location and extents in the considered structure.

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Metadata
Title
Damage Assessment in Beam-Like Structures Using Cuckoo Search Algorithm and Experimentally Measured Data
Authors
H. Tran-Ngoc
S. Khatir
G. De Roeck
T. Bui-Tien
M. Abdel Wahab
Copyright Year
2020
Publisher
Springer Singapore
Book
Proceedings of the 13th International Conference on Damage Assessment of Structures

Print ISBN: 978-981-13-8330-4

Electronic ISBN: 978-981-13-8331-1

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-13-8331-1_27

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