Cooperative Coevolutionary Genetic Algorithm for Vibration-Based Damage Detection in Plates
Abstract
Vibration-based damage detection, a nondestructive method, is based on the fact that vibration characteristics such as natural frequencies and mode shapes of structures are changed when the damage occurs. This paper presents cooperative coevolutionary genetic algorithm (CCGA) which can be used to optimize problems with a large number of decision variables, as the optimizer for the vibration-based damage detection in plates. The objective function is a numerical indicator calculated from the vibration characteristics of the actual damage and mass matrix and stiffness matrix resulting from the predicted damage. The finite element method is used for the objective calculation in which the plates are divided into 100 elements. There are 2 test problems with different damage occurred in the plates. In each problem, 4 cases of various boundary conditions are used. The simulation results reveal that CCGA apparently outperforms genetic algorithm (GA) and particle swarm optimization (PSO) for all test cases. In addition, solutions obtained from using CCGA show that CCGA can accurately identify the damage presented in the plates although the algorithm uses small number of generated solutions in solution search. Keywords : vibration-based damage detection, genetic algorithm, co-operative co-evolution, plate structure, finite element methodReferences
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Bergh, F. V. D., & Engelbrecht, A. P. (2004). A Cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 225-239.
Boonlong, K. (2014). Vibration-based damage detection in beams by cooperative coevolutionary genetic algorithm. Advances in Mechanical Engineering, 2014, 1-13.
Boonlong, K., Chaiyaratana, N., & Kuntanapreeda, S. (2004). Optimal control of a hysteresis system by means of co-operative co-evolution. International Journal of Computational Intelligence and Applications, 4(4), 321-336.
Braun, C. E., Chiwiacowsky, L. D., & Gómez, A. T. (2015). Variations of ant colony optimization for the solution of the structural damage identification. Procedia Computer Science, 51, 875-884.
Chou, J. H., & Ghaboussi, J. (2001). Genetic algorithm in structural damage detection. Computers & Structures, 79(4), 1335-1353.
Dawe, D. J. (1984). Matrix and finite element displacement analysis and structures. Oxford engineering science series, 410-541.
Deb, K. (1997). Mechanical component design using genetic algorithms. In D. Dasgupta, & Z. Michalewicz. (Eds.), Evolutionary Algorithms in Engineering Applications. (pp. 495-512), New York: Springer.
Deb, K., & Agrawal R. B. (1995). Simulated binary crossover for continuous search space. Complex Systems, 9(2), 115-148.
Ding, Z. H., Huang, M., & Lu, Z. R. (2016). Structural damage detection using artificial bee colony algorithm with hybrid search strategy. Swarm and Evolutionary Computation, 28, 1-13.
Galewski, M. A. (2016). Spectrum-based modal parameters identification with particle swarm optimization. Mechatronics, 37, 21-32.
Garcia-Pedrajas, N. (2003). COVNET: a cooperative coevolutionary model for evolving artificial neural networks. IEEE Transactions on Neural Networks, 14(3), 575-596.
Gawronski, W., & Sawicki, J. T. (2000). Structural damage detection using modal norms. Journal of Sound and Vibration, 229(1), 194-198.
Gerist, S., & Maheri, M. R. (2016). Multi-stage approach for structural damage detection problem using basis pursuit and particle swarm optimization. Journal of Sound and Vibration, 384, 210-226.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning, Addison-Wesley, USA.
González, A. G., & Fassois, S. D. (2016). A supervised vibration-based statistical methodology for damage detection under varying environmental conditions & its laboratory assessment with a scale wind turbine blade. Journal of Sound and Vibration, 366, 484-500.
Guo, H. Y., & Li, Z. L. (2012). Structural damage identification based on Bayesian theory and improved immune genetic algorithm. Expert Systems with Applications, 39(7), 6426-6434.
He, R. S., & Hwang, S. F. (2006). Damage detection by an adaptive real-parameter simulated annealing genetic algorithm. Computers & Structures, 84(31-32), 2231-2243.
Holland, J. H. (1975). Adaptation in natural and artificial systems, Ann Arbour, MI: University of Michigan Press, USA.
Ibáñez, O., Cordón, O., & Damas, S. (2012). A cooperative coevolutionary approach dealing with the skull–face overlay uncertainty in forensic identification by craniofacial superimposition. Soft Computing, 16(5), 797-808.
Kennedy, J. & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Network. (pp. 1942-1948). Perth, Australia.
Kim, N. I. & Lee, J. (2013). Damage identification of trusses with elastic supports using FEM and genetic algorithm. Advances in Mechanical Engineering, 2013, 1-10.
Kim, V. & Boonlong, K. (2015) Cooperative coevolutionary genetic algorithm for vibration based damage detection of truss structures. International Journal of Advanced and Applied Science, 2(12), 52-57.
Li, X. & Yao, X. (2012). Cooperatively coevolving particle swarms for large scale optimization. IEEE Transactions on Evolutionary Computation, 16(2), 210-224.
Majumdar, A., Maiti, D. K., & Maity, D. (2012). Damage assessment of truss structures from changes in natural frequencies using ant colony optimization. Applied Mathematics and Computation, 218(19), 9759-9772.
Mares, C., & Surace, C. (1996). An application of genetic algorithms to identify damage in elastic structures. Journal of Sound and Vibration, 195(2), 195-215.
Nobaharia, M., & Seyedpoorb, S. M. (2011). Structural damage detection using an efficient correlation-based index and a modified genetic algorithm. Mathematical and Computer Modelling, 53(9-10), 1798-1809.
Panigrahi, S. K., Chakraverty, S. & Mishra, B. K. (2009). Vibration based damage detection in a uniform strength beam using genetic algorithm. Meccanica, 44(6), 697-710.
Pereraa, R., Marina, R., & Ruizb, A. (2013). Static–dynamic multi-scale structural damage identification in a multi-objective framework. Journal of Sound and Vibration, 332(6), 1484-1500.
Potter, M. A., & de Jong, K. A. (1994). A cooperative coevolutionary approach to function optimization. Lecture Notes in Computer Science, 866, 249-257.
Potter, M. A., & de Jong, K. A. (2000). Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation, 8(1) (2000) 1-29.
Rao, M. A., Srinivas, J., & Murthy, B. S. N. (2004). Damage detection in vibrating bodies using genetic algorithms. Computers & Structures, 82(11-12), 963-968.
Shih, H. W., Thambiratnam, D. P., & Chan, T. H. T. (2009). Vibration based structural damage detection in flexural members using multi-criteria approach. Journal of Sound and Vibration, 323(3–5), 645-661.
Yang, Q. W., & Liu, J. K. (2007). Structural damage identification based on residual force vector, Journal of Sound and Vibration, 305(1-2), 298-307.
Yin, T., Jiang, Q. H., & Yuen, K. V. (2017). Vibration-based damage detection for structural connections using incomplete modal data by Bayesian approach and model reduction technique. Engineering Structures, 132, 260-277.
Zhong, F., Li, H., & Zhong S. (2017). An improved artificial bee colony algorithm with modified-neighborhood-based update operator and independent-inheriting-search strategy for global optimization. Engineering Applications of Artificial Intelligence, 58, 134-156.
Bergh, F. V. D., & Engelbrecht, A. P. (2004). A Cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 225-239.
Boonlong, K. (2014). Vibration-based damage detection in beams by cooperative coevolutionary genetic algorithm. Advances in Mechanical Engineering, 2014, 1-13.
Boonlong, K., Chaiyaratana, N., & Kuntanapreeda, S. (2004). Optimal control of a hysteresis system by means of co-operative co-evolution. International Journal of Computational Intelligence and Applications, 4(4), 321-336.
Braun, C. E., Chiwiacowsky, L. D., & Gómez, A. T. (2015). Variations of ant colony optimization for the solution of the structural damage identification. Procedia Computer Science, 51, 875-884.
Chou, J. H., & Ghaboussi, J. (2001). Genetic algorithm in structural damage detection. Computers & Structures, 79(4), 1335-1353.
Dawe, D. J. (1984). Matrix and finite element displacement analysis and structures. Oxford engineering science series, 410-541.
Deb, K. (1997). Mechanical component design using genetic algorithms. In D. Dasgupta, & Z. Michalewicz. (Eds.), Evolutionary Algorithms in Engineering Applications. (pp. 495-512), New York: Springer.
Deb, K., & Agrawal R. B. (1995). Simulated binary crossover for continuous search space. Complex Systems, 9(2), 115-148.
Ding, Z. H., Huang, M., & Lu, Z. R. (2016). Structural damage detection using artificial bee colony algorithm with hybrid search strategy. Swarm and Evolutionary Computation, 28, 1-13.
Galewski, M. A. (2016). Spectrum-based modal parameters identification with particle swarm optimization. Mechatronics, 37, 21-32.
Garcia-Pedrajas, N. (2003). COVNET: a cooperative coevolutionary model for evolving artificial neural networks. IEEE Transactions on Neural Networks, 14(3), 575-596.
Gawronski, W., & Sawicki, J. T. (2000). Structural damage detection using modal norms. Journal of Sound and Vibration, 229(1), 194-198.
Gerist, S., & Maheri, M. R. (2016). Multi-stage approach for structural damage detection problem using basis pursuit and particle swarm optimization. Journal of Sound and Vibration, 384, 210-226.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning, Addison-Wesley, USA.
González, A. G., & Fassois, S. D. (2016). A supervised vibration-based statistical methodology for damage detection under varying environmental conditions & its laboratory assessment with a scale wind turbine blade. Journal of Sound and Vibration, 366, 484-500.
Guo, H. Y., & Li, Z. L. (2012). Structural damage identification based on Bayesian theory and improved immune genetic algorithm. Expert Systems with Applications, 39(7), 6426-6434.
He, R. S., & Hwang, S. F. (2006). Damage detection by an adaptive real-parameter simulated annealing genetic algorithm. Computers & Structures, 84(31-32), 2231-2243.
Holland, J. H. (1975). Adaptation in natural and artificial systems, Ann Arbour, MI: University of Michigan Press, USA.
Ibáñez, O., Cordón, O., & Damas, S. (2012). A cooperative coevolutionary approach dealing with the skull–face overlay uncertainty in forensic identification by craniofacial superimposition. Soft Computing, 16(5), 797-808.
Kennedy, J. & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Network. (pp. 1942-1948). Perth, Australia.
Kim, N. I. & Lee, J. (2013). Damage identification of trusses with elastic supports using FEM and genetic algorithm. Advances in Mechanical Engineering, 2013, 1-10.
Kim, V. & Boonlong, K. (2015) Cooperative coevolutionary genetic algorithm for vibration based damage detection of truss structures. International Journal of Advanced and Applied Science, 2(12), 52-57.
Li, X. & Yao, X. (2012). Cooperatively coevolving particle swarms for large scale optimization. IEEE Transactions on Evolutionary Computation, 16(2), 210-224.
Majumdar, A., Maiti, D. K., & Maity, D. (2012). Damage assessment of truss structures from changes in natural frequencies using ant colony optimization. Applied Mathematics and Computation, 218(19), 9759-9772.
Mares, C., & Surace, C. (1996). An application of genetic algorithms to identify damage in elastic structures. Journal of Sound and Vibration, 195(2), 195-215.
Nobaharia, M., & Seyedpoorb, S. M. (2011). Structural damage detection using an efficient correlation-based index and a modified genetic algorithm. Mathematical and Computer Modelling, 53(9-10), 1798-1809.
Panigrahi, S. K., Chakraverty, S. & Mishra, B. K. (2009). Vibration based damage detection in a uniform strength beam using genetic algorithm. Meccanica, 44(6), 697-710.
Pereraa, R., Marina, R., & Ruizb, A. (2013). Static–dynamic multi-scale structural damage identification in a multi-objective framework. Journal of Sound and Vibration, 332(6), 1484-1500.
Potter, M. A., & de Jong, K. A. (1994). A cooperative coevolutionary approach to function optimization. Lecture Notes in Computer Science, 866, 249-257.
Potter, M. A., & de Jong, K. A. (2000). Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation, 8(1) (2000) 1-29.
Rao, M. A., Srinivas, J., & Murthy, B. S. N. (2004). Damage detection in vibrating bodies using genetic algorithms. Computers & Structures, 82(11-12), 963-968.
Shih, H. W., Thambiratnam, D. P., & Chan, T. H. T. (2009). Vibration based structural damage detection in flexural members using multi-criteria approach. Journal of Sound and Vibration, 323(3–5), 645-661.
Yang, Q. W., & Liu, J. K. (2007). Structural damage identification based on residual force vector, Journal of Sound and Vibration, 305(1-2), 298-307.
Yin, T., Jiang, Q. H., & Yuen, K. V. (2017). Vibration-based damage detection for structural connections using incomplete modal data by Bayesian approach and model reduction technique. Engineering Structures, 132, 260-277.
Zhong, F., Li, H., & Zhong S. (2017). An improved artificial bee colony algorithm with modified-neighborhood-based update operator and independent-inheriting-search strategy for global optimization. Engineering Applications of Artificial Intelligence, 58, 134-156.
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2017-09-07
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