Hybrid TLBO and BFGS for structural health monitoring optimisation problems
- 10.2991/ismems-16.2016.35How to use a DOI?
- Structural health monitoring, Meta-heuristics, Modal data, Damage detection
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
- © 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 - 10.2991/ismems-16.2016.35 ID - Pholdee2016/12 ER -