Vibration Based Structural Damage Detection Technique using Particle Swarm Optimization with Incremental Swarm Size

A simple and robust methodology is presented to determine the location and amount of crack in beam like structures based on the incremental particle swarm optimization technique. A comparison is made for assessing the performance of standard particle swarm optimization and the incremental particle swarm optimization technique for detecting crack in structural members. The objective function is formulated using the measured natural frequency of the intact structure and the frequency obtained from the finite element simulation. The outcomes of the simulated results demonstrate that the developed method is capable of detecting and estimating the extent of damages with satisfactory precision.

[1]  Charles R. Farrar,et al.  Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review , 1996 .

[2]  Damodar Maity,et al.  Damage assessment of truss structures from changes in natural frequencies using ant colony optimization , 2012, Appl. Math. Comput..

[3]  Sung N. Jung,et al.  Validation of HART II Structural Dynamics Predictions Based on Prescribed Airloads , 2012 .

[4]  Damodar Maity,et al.  Damage assessment of structures from changes in natural frequencies using genetic algorithm , 2005 .

[5]  Ricardo Perera,et al.  A multistage FE updating procedure for damage identification in large-scale structures based on multiobjective evolutionary optimization , 2008 .

[6]  Damodar Maity,et al.  Application of Radial Basis Neural Network on Damage Assessment of Structures , 2011 .

[7]  E. J. Williams,et al.  STRUCTURAL DAMAGE DETECTION BY A SENSITIVITY AND STATISTICAL-BASED METHOD , 1998 .

[8]  E. Peter Carden,et al.  Vibration Based Condition Monitoring: A Review , 2004 .

[9]  Thomas Stützle,et al.  Incremental Social Learning in Particle Swarms , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Kamran Behdinan,et al.  Particle swarm approach for structural design optimization , 2007 .

[11]  Maja J. Mataric,et al.  Learning social behavior , 1997, Robotics Auton. Syst..

[12]  Peng Xu,et al.  Structural health monitoring based on continuous ACO method , 2011, Microelectron. Reliab..

[13]  O. S. Salawu.  Detection of structural damage through changes in frequency: a review , 1997 .

[14]  Hong Hao,et al.  Vibration based damage detection using artificial neural network with consideration of uncertainties , 2007 .

[15]  Hong Hao,et al.  Vibration-based Damage Detection of Structures by Genetic Algorithm , 2002 .

[16]  Antonio Ruiz,et al.  Application of particle swarm optimization and genetic algorithms to multiobjective damage identification inverse problems with modelling errors , 2010 .

[17]  Peter Cawley,et al.  Non-destructive testing—current capabilities and future directions , 2001 .

[18]  A. Barr,et al.  One-dimensional theory of cracked Bernoulli-Euler beams , 1984 .

[19]  V. Meruane,et al.  An hybrid real genetic algorithm to detect structural damage using modal properties , 2011 .

[20]  Shapour Moradi,et al.  On the application of bees algorithm to the problem of crack detection of beam-type structures , 2011 .

[21]  Pizhong Qiao,et al.  Vibration-based Damage Identification Methods: A Review and Comparative Study , 2011 .

[22]  Damodar Maity,et al.  Damage assessment of structures using hybrid neuro-genetic algorithm , 2007, Appl. Soft Comput..

[23]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[24]  Min-Jea Tahk,et al.  Nonlinear adaptive velocity controller design for an air-breathing supersonic engine , 2012 .

[25]  José Elias Laier,et al.  A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification , 2009, Adv. Eng. Softw..