Hybrid TLBO and BFGS for structural health monitoring optimisation problems

DOI

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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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  - 10.2991/ismems-16.2016.35
ID  - Pholdee2016/12
ER  -