Hybrid TLBO and BFGS for structural health monitoring optimisation problems

被引：0

作者：

Pholdee, Nantiwat ^{[1
]}

Bureerat, Sujin ^{[1
]}

机构：

[1] Khon Kaen Univ, Dept Mech Engn, Sustainable & Infrastruct Res & Dev Ctr, Fac Engn, Khon Kaen 40002, Thailand

来源：

PROCEEDINGS OF THE INTERNATIONAL SYMPOSIUM ON MECHANICAL ENGINEERING AND MATERIAL SCIENCE | 2016年 / 93卷

关键词：

Structural health monitoring; Meta-heuristics; Modal data; Damage detection; DAMAGE DETECTION; NATURAL FREQUENCIES; TRUSS STRUCTURES; SENSITIVITY;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：199 / 204

页数：6

共 17 条

[1] Nonparametric Structural Damage Detection Algorithm for Ambient Vibration Response: Utilizing Artificial Neural Networks and Self-Organizing Maps [J].