Commenced in January 2007
Paper Count: 32009
Probability-Based Damage Detection of Structures Using Model Updating with Enhanced Ideal Gas Molecular Movement Algorithm
Abstract:Model updating method has received increasing attention in damage detection structures based on measured modal parameters. Therefore, a probability-based damage detection (PBDD) procedure based on a model updating procedure is presented in this paper, in which a one-stage model-based damage identification technique based on the dynamic features of a structure is investigated. The presented framework uses a finite element updating method with a Monte Carlo simulation that considers the uncertainty caused by measurement noise. Enhanced ideal gas molecular movement (EIGMM) is used as the main algorithm for model updating. Ideal gas molecular movement (IGMM) is a multiagent algorithm based on the ideal gas molecular movement. Ideal gas molecules disperse rapidly in different directions and cover all the space inside. This is embedded in the high speed of molecules, collisions between them and with the surrounding barriers. In IGMM algorithm to accomplish the optimal solutions, the initial population of gas molecules is randomly generated and the governing equations related to the velocity of gas molecules and collisions between those are utilized. In this paper, an enhanced version of IGMM, which removes unchanged variables after specified iterations, is developed. The proposed method is implemented on two numerical examples in the field of structural damage detection. The results show that the proposed method can perform well and competitive in PBDD of structures.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1128875Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 942
 C. Boller, F.-K. Chang, and Y. Fujino, Encyclopedia of structural health monitoring. John Wiley & Sons, 2009.
 R. Ghiasi, P. Torkzadeh, and M. Noori, “A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function,” Struct. Heal. Monit., vol. 15, no. 3, pp. 302–316, May 2016.
 E. Simoen, G. De Roeck, and G. Lombaert, “Dealing with uncertainty in model updating for damage assessment: A review,” Mech. Syst. Signal Process., pp. 1–27, 2014.
 Y. X. and S. W. X.J. Wang, X.Q. Zhou, “Comparisons between Modal- Parameter-Based and Flexibility-Based Damage Identification Methods,” Adv. Struct. Eng., vol. 16, no. September, 2013.
 A. Messina, E. J. Williams, and T. Contursi, “Structural damage detection by a sensitivity and statistical-based method,” J. Sound Vib., vol. 216, no. 5, pp. 791–808, 1998.
 Y. Xu, Y. Qian, J. Chen, and G. Song, “Probability-based damage detection using model updating with efficient uncertainty propagation,” Mech. Syst. Signal Process., pp. 1–13, 2015.
 N. Bakhary, H. Hao, and A. J. Deeks, “Damage detection using artificial neural network with consideration of uncertainties,” Eng. Struct., vol. 29, no. 11, pp. 2806–2815, Nov. 2007.
 M. R. Ghasemi and H. Varaee, “A fast multi-objective optimization using an efficient ideal gas molecular movement algorithm,” Eng. Comput., pp. 1–20, 2016.
 H. Varaee and M. R. Ghasemi, “Engineering optimization based on ideal gas molecular movement algorithm,” Eng. Comput., pp. 1–23, 2016.
 R. Ghiasi, M. R. Ghasemi, M. Noori, “Comparison of Seven Artificial Intelligence Methods for Damage Detection of Structures,” Proceedings of the Fifteenth International Conference on Civil, Structural and Environment al Engineering Computing (CC2015), Stirlingshire, Scotland, paper 116, 2015.
 R. Ghiasi, P. Torkzadeh, and M. Noori, “Structural damage detection using artificial neural networks and least square support vector machine with particle swarm harmony search algorithm,” Int. J. Sustain. Mater. Struct. Syst., vol. 1, no. 4, pp. 303–320, 2014.
 S. M. Seyedpoor, “A two stage method for structural damage detection using a modal strain energy based index and particle swarm optimization,” Int. J. Non. Linear. Mech., vol. 47, no. 1, pp. 1–8, 2012.
 X. G. Hua, Y. Q. Ni, Z. Q. Chen, and J. M. Ko, “An improved perturbation method for stochastic finite element model updating,” Int. J. Numer. Methods Eng., vol. 73, no. 13, pp. 1845–1864, 2008.
 H. Hao and Y. Xia, “Vibration-based damage detection of structures by genetic algorithm,” J. Comput. Civ. Eng., vol. 16, no. 3, pp. 222–229, 2002.
 N. T. Kottegoda and R. Rosso, Probability, Statistics, and Reliability for Civil and Environmental Engineers. The McGraw-Hill Companies, 1997.
 P. Torkzadeh, Y. Goodarzi, and E. Salajegheh, “A two-stage damage detection method for large-scale structures by kinetic and modal strain energies using heuristic particle swarm optimization,” Int. J. Optim. Civ. Eng., vol. 3, no. 3, pp. 465–482, 2013.
 A. Kaveh, S. M. Javadi, and M. Maniat, “Damage Assessment via Modal Data with a Mixed Particle Swarm Strategy, Ray Optimizer, and Harmony Search,” Asian J. Civ. Eng., vol. 15, no. 1, pp. 95–106, 2014.