A comparative study on crack identification of structures from the changes in natural frequencies using GA and PSO

A comparative study on crack identification of structures from the changes in natural frequencies... Purpose – The early detection of cracks, corrosion and structural failure in aging structures is one of the major challenges in the civil, mechanical and aircraft industries. Common inspection techniques are time consuming and hence can have strong economic implications due to downtime. The paper aims to discuss these issues. Design/methodology/approach – As a result, during the past decade a number of methodologies have been proposed for detecting crack in structure based on variations in the structure's dynamic characteristics. This work showcases the efficacy of particle swarm optimization (PSO) and genetic algorithm (GA) in damage assessment of structures. Findings – Efficiency of these tools has been tested on structures like beam, plane and space truss. The results show the effectiveness of PSO in crack identification and the possibility of implementing it in a real-time structural health monitoring system for aircraft and civil structures. Originality/value – The methodology presented establishes the PSO as robust and competent tool over GA for crack identification using changes in natural frequencies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Engineering Computations Emerald Publishing

A comparative study on crack identification of structures from the changes in natural frequencies using GA and PSO

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0264-4401
DOI
10.1108/EC-02-2013-0061
Publisher site
See Article on Publisher Site

Abstract

Purpose – The early detection of cracks, corrosion and structural failure in aging structures is one of the major challenges in the civil, mechanical and aircraft industries. Common inspection techniques are time consuming and hence can have strong economic implications due to downtime. The paper aims to discuss these issues. Design/methodology/approach – As a result, during the past decade a number of methodologies have been proposed for detecting crack in structure based on variations in the structure's dynamic characteristics. This work showcases the efficacy of particle swarm optimization (PSO) and genetic algorithm (GA) in damage assessment of structures. Findings – Efficiency of these tools has been tested on structures like beam, plane and space truss. The results show the effectiveness of PSO in crack identification and the possibility of implementing it in a real-time structural health monitoring system for aircraft and civil structures. Originality/value – The methodology presented establishes the PSO as robust and competent tool over GA for crack identification using changes in natural frequencies.

Journal

Engineering ComputationsEmerald Publishing

Published: Sep 30, 2014

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