A two-stage damage detection approach based on subset selection and genetic algorithms

A two-stage damage detection method is proposed and demonstrated for structural health monitoring. In the first stage, the subset selection method is applied for the identification of the multiple damage locations. In the second stage, the damage severities of the identified damaged elements are determined applying SSGA to solve the optimization problem. In this method, the sensitivities of residual force vectors with respect to damage parameters are employed for the subset selection process. This approach is particularly efficient in detecting multiple damage locations. The SEREP is applied as needed to expand the identified mode shapes while using a limited number of sensors. Uncertainties in the stiffness of the elements are also considered as a source of modeling errors to investigate their effects on the performance of the proposed method in detecting damage in real-life structures. Through a series of illustrative examples, the proposed two-stage damage detection method is demonstrated to be a reliable tool for identifying and quantifying multiple damage locations within diverse structural systems.

[1]  Jin-Hak Yi,et al.  Vibration-based damage detection in beams using genetic algorithm , 2007 .

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

[3]  Michael I Friswell,et al.  Damage identification using inverse methods , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[4]  Seamus D. Garvey,et al.  A COMBINED GENETIC AND EIGENSENSITIVITY ALGORITHM FOR THE LOCATION OF DAMAGE IN STRUCTURES , 1998 .

[5]  Alan J. Miller.  The Convergence of Efroymson's Stepwise Regression Algorithm , 1996 .

[6]  Billie F. Spencer,et al.  Damage detection in ambient vibration using proportional flexibility matrix with incomplete measured DOFs , 2007 .

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

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  Wei Song,et al.  Improved Damage Localization and Quantification Using Subset Selection , 2009 .

[10]  Ahmet E. Aktan,et al.  Bridge-condition assessment by modal flexibility , 1994 .

[11]  Guido De Roeck,et al.  Damage assessment by FE model updating using damage functions , 2002 .

[12]  R. Fox,et al.  Rates of change of eigenvalues and eigenvectors. , 1968 .

[13]  L. Darrell Whitley,et al.  The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best , 1989, ICGA.

[14]  Shirley J. Dyke,et al.  A parameter subset selection method using residual force vector for detecting multiple damage locations , 2010 .

[15]  Gilbert Syswerda,et al.  A Study of Reproduction in Generational and Steady State Genetic Algorithms , 1990, FOGA.

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

[17]  John E. Mottershead,et al.  Finite Element Model Updating in Structural Dynamics , 1995 .

[18]  John E. Mottershead,et al.  Model Updating In Structural Dynamics: A Survey , 1993 .

[19]  Branislav Titurus,et al.  Damage detection using generic elements: Part II. Damage detection , 2003 .

[20]  Ning Hu,et al.  Damage assessment of structures using modal test data , 2001 .

[21]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[22]  D. Bernal.  Load Vectors for Damage Localization , 2002 .

[23]  Seamus D. Garvey,et al.  Parameter subset selection in damage location , 1997 .