DAMAGE DETECTION IN SIMULATED SPACE FRAMES USING GENETIC ALGORITHMS

Genetic algorithms (GA) based finite element model updating are applied to predict damage location and severity in space frames. The changes in natural frequencies are used as dynamic indicators to describe damaged members. Objective functions including dynamic data provide minimization of dynamic differences between numerical model and simulated damaged model. The presence of damages in structural elements is identified by stiffness reduction as a reduction in modulus of elasticity. Reproduction, double-point crossover and mutation operators are used in GA optimization procedures. In this paper, different simulated examples having various damage scenarios are modelled in SAP2000 software to obtain the experimental dynamic data. In the last example, noise effect is taken into account in simulated damaged data. A program is developed in MATLAB software for numerical model updating based on all genetic algorithm procedures. Thus, the size and extent of simulated damages are determined by updated numerical model. Results obtained from examples show that GA optimization is a convenient method for damage identification.

Keywords:

FEM, genetic algorithm dynamic analysis, damage detection.,

PDF

___

[1] C. Mares, C. Surace, An application of genetic algorithms to identify damage in elastic structures, J. Sound Vib. 195, 195-215, 1996.
[2] J.-H. Chou, J. Ghaboussi, Genetic algorithm in structural damage detection, Comput. Struct. 79, 1335-1353, 2001.
[3] A. Dutta, S. Talukdar, Damage detection in bridges using accurate modal parameters, Finite Elem. Anal. Des. 40, 287–304, 2004.
[4] M. A. Rao, J. Srinivas, B.S.N. Murthy, Damage detection in vibrating bodies using genetic algorithms, Comput. Struct. 82, 963–968, 2004.
[5] B. Jaishi, W.-Xin Ren, Damage detection by finite element model updating using modal flexibility residual, J. Sound Vib. 290, 369–387, 2006.
[6] R. Perera, R. Torres, Structural damage detection via modal data with genetic algorithms, J. Struct. Eng. ASCE 132, 1491–1501, 2006.
[7] H.M. Gomes, N.R.S. Silva, Some comparisons for damage detection on structures using genetic algorithms and modal sensitivity method, Appl. Math. Model. 32,2216–2232, 2008.
[8] A. Esfandiari, F. Bakhtiari-Nejad, A. Rahai, M. Sanayei, Structural model updating using frequency response function and quasi-linear sensitivity equation, J.Sound Vib. 326,557–573, 2009.
[9] H. Liu, K. Xin, Q. Qi, Study of structural damage detection with multi-objective function genetic algorithms, Procedia Engineering 12,80–86, 2011.
[10] S. H. Sim, B. F. Spencer Jr., T. Nagayama, Multimetric sensing for structural damage detection, J. Eng. Mech.ASCE 137, 22-30, 2011.
[11] F. Khoshnoudian, A. Esfandiari, Structural damage diagnosis using modal data, Scientia Iranica A 18, 853–860, 2011.
[12] S.M. Pourhoseini Nejad, Gh. R. Ghodrati Amiri, A. Asadi, E. Afshari, Z. Tabrizian, Damage detection of skeletal structures using particle swarm optimizer with passive congregation (PSOPC) algorithm via incomplete modal data, Comp. Meth. Civil Eng. 3, 1-13, 2012.
[13] A. Majumdar, D. K. Maiti, D. Maity, Damage assessment of truss structures from changes in natural frequencies using ant colony optimization, Appl. Math. Comput. 218, 9759–9772, 2012.
[14] E. Asnaashari, J. K. Sinha, Crack detection in structures using deviation from normal distribution of measured vibration responses, J. Sound Vib. 333, 4139–4151, 2014.
[15] T. Tang, X. Xie, Structural damage identification using virtual flexibility matrix I: simulation verification, Solids and Structures (SAS), 3, 1-8, 2014.
[16] MATLAB, The language of technical computing, The Mathworks Inc., Natick, USA, 2009.
[17] SAP2000, Integrated software for structural analysis and design, Computers and Structures Inc., Berkeley, CA, USA, 2008.
[18] D. E. Goldberg, J. H. Holland, Genetic algorithms and machine learning, Kluwer Academic Publishers, Machine Learning 3, 95-99, 1988.
[19] Y.S. Erdogan, Damage detection and parameter identification by finite element model updating using genetic algorithms, Master Thesis, Istanbul Technical University, Istanbul, 2007.
[20] A. Topcu, Computer aided numerical analysis lecture notes, Eskişehir Osmangazi University, 2013.
[21] B.A. Zarate, J.M. Caicedo, Finite element model updating: Multiple alternatives, Eng. Struct. 30, 3724-3730, 2008.
[22] M. Armutçu, Optimum design of plane frames using genetic algorithm, Master Thesis, Karadeniz Technical University, Trabzon, 1997.
[23] Z. Aydın, Minimum weighted optimization of plane trusses with genetic algorithm, Graduation Thesis, Karadeniz Technical University, Trabzon, 1997.
[24] A. Daloğlu, Z.Aydın, Optimum design of trusses for practical applications, Pamukkale University Journal of Engineering Sciences 5, 951-957, 1999.
[25] S. Bekiroğlu, Optimum design of steel frame with genetic algorithm, Master Thesis, Karadeniz Technical University, Trabzon, 2003.
[26] W.-X. Ren, G. D. Roeck, Structural damage identification using modal data. I: Simulation Verification, J. Struct. Eng. ASCE 128,87-95, 2002.
[27] S. Aktaşoğlu, Damage detection in beam-like structures via combined genetic algorithm and non-linear optimisation, Master Thesis, Middle East Technical University, Ankara,2012.

Sigma Mühendislik ve Fen Bilimleri Dergisi-Cover

ISSN: 1304-7191
Yayın Aralığı: Yılda 4 Sayı
Yayıncı: Yıldız Teknik Üniversitesi

Arşiv