Proceedings of the International Symposium on Mechanical Engineering and Material Science

Hybrid TLBO and BFGS for structural health monitoring optimisation problems

Authors
Nantiwat Pholdee, Sujin Bureerat
Corresponding Author
Nantiwat Pholdee
Available Online December 2016.
DOI
https://doi.org/10.2991/ismems-16.2016.35How to use a DOI?
Keywords
Structural health monitoring, Meta-heuristics, Modal data, Damage detection
Abstract

This paper proposes hybrid teaching learning based optimisation (TLBO) and the Broyden-Fletcher-Goldfarb-Shanno algorithm (BFGS) for solving structural health monitoring optimisation problems. Two structural damage detection problems from two different truss structures are used to examine the search performance of the proposed algorithm while several well establish meta-heuristics (MHs) are used for benchmarking. The results indicated that the proposed algorithm is superior to the others. This study clarified that integrating BFGS into TLBO leads to increasing search performance of the new hybrid algorithm for solving structural health monitoring optimisation problems

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the International Symposium on Mechanical Engineering and Material Science
Series
Advances in Engineering Research
Publication Date
December 2016
ISBN
978-94-6252-277-0
ISSN
2352-5401
DOI
https://doi.org/10.2991/ismems-16.2016.35How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Nantiwat Pholdee
AU  - Sujin Bureerat
PY  - 2016/12
DA  - 2016/12
TI  - Hybrid TLBO and BFGS for structural health monitoring optimisation problems
BT  - Proceedings of the International Symposium on Mechanical Engineering and Material Science
PB  - Atlantis Press
SP  - 199
EP  - 204
SN  - 2352-5401
UR  - https://doi.org/10.2991/ismems-16.2016.35
DO  - https://doi.org/10.2991/ismems-16.2016.35
ID  - Pholdee2016/12
ER  -