A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification

This study proposes a new PSOS-model based damage identification procedure using frequency domain data. The formulation of the objective function for the minimization problem is based on the Frequency Response Functions (FRFs) of the system. A novel strategy for the control of the Particle Swarm Optimization (PSO) parameters based on the Nelder-Mead algorithm (Simplex method) is presented; consequently, the convergence of the PSOS becomes independent of the heuristic constants and its stability and confidence are enhanced. The formulated hybrid method performs better in different benchmark functions than the Simulated Annealing (SA) and the basic PSO (PSO"b). Two damage identification problems, taking into consideration the effects of noisy and incomplete data, were studied: first, a 10-bar truss and second, a cracked free-free beam, both modeled with finite elements. In these cases, the damage location and extent were successfully determined. Finally, a non-linear oscillator (Duffing oscillator) was identified by PSOS providing good results.

[1]  Grant P. Steven,et al.  VIBRATION-BASED MODEL-DEPENDENT DAMAGE (DELAMINATION) IDENTIFICATION AND HEALTH MONITORING FOR COMPOSITE STRUCTURES — A REVIEW , 2000 .

[2]  Norris Stubbs,et al.  Damage identification in beam-type structures: frequency-based method vs mode-shape-based method , 2003 .

[3]  Jonathan A. Wright,et al.  A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization , 2004 .

[4]  Bernhard Schmitz,et al.  Review of non-destructive measuring methods for the assessment of surface integrity : a survey of new measuring methods for coatings, layered structures and processed surfaces , 1999 .

[5]  Jyoti K. Sinha,et al.  SIMPLIFIED MODELS FOR THE LOCATION OF CRACKS IN BEAM STRUCTURES USING MEASURED VIBRATION DATA , 2002 .

[6]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[7]  Keith Worden,et al.  An Overview of Intelligent Fault Detection in Systems and Structures , 2004 .

[8]  P. Fourie,et al.  The particle swarm optimization algorithm in size and shape optimization , 2002 .

[9]  X. Fang,et al.  Structural damage detection using neural network with learning rate improvement , 2005 .

[10]  K. Moslem,et al.  Structural Damage Detection by Genetic Algorithms , 2002 .

[11]  Kwok-Wing Chau.  Algal Bloom Prediction with Particle Swarm Optimization Algorithm , 2005, CIS.

[12]  G. M. L. Gladwell,et al.  Inverse Problems in Vibration , 1986 .

[13]  Chung Bang Yun,et al.  Neural networks-based damage detection for bridges considering errors in baseline finite element models , 2003 .

[14]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[15]  P. N. Saavedra,et al.  Crack detection and vibration behavior of cracked beams , 2001 .

[16]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[17]  Mauro J. Atalla,et al.  Model updating using neural networks , 1996 .

[18]  Kwok-wing Chau,et al.  Particle Swarm Optimization Training Algorithm for ANNs in Stage Prediction of Shing Mun River , 2006 .

[19]  Jaroslaw Sobieszczanski-Sobieski,et al.  Particle swarm optimization , 2002 .

[20]  Kye-Si Kwon,et al.  Robust finite element model updating using Taguchi method , 2005 .

[21]  Sandro Ridella,et al.  Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithmCorrigenda for this article is available here , 1987, TOMS.

[22]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[23]  P. Hagedorn.  Non-Linear Oscillations , 1982 .

[24]  Keith Worden,et al.  STRUCTURAL FAULT DETECTION USING A NOVELTY MEASURE , 1997 .

[25]  Charles R. Farrar,et al.  A summary review of vibration-based damage identification methods , 1998 .

[26]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[27]  William L. Goffe,et al.  SIMANN: FORTRAN module to perform Global Optimization of Statistical Functions with Simulated Annealing , 1992 .

[28]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[29]  Charles R. Farrar,et al.  Novelty detection in a changing environment: Regression and interpolation approaches , 2002 .

[30]  Albert A. Groenwold,et al.  Sizing design of truss structures using particle swarms , 2003 .

[31]  B.S.N. Murthy,et al.  Damage detection in vibrating bodies using genetic algorithms , 2004 .

[32]  M. R. AlRashidi,et al.  A Survey of Particle Swarm Optimization Applications in Power System Operations , 2006 .

[33]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..