Probability-Based Damage Detection of Structures Using Kriging Surrogates and Enhanced Ideal Gas Molecular Movement Algorithm
Surrogate model has received increasing attention for use in detecting damage of structures based on vibration modal parameters. However, uncertainties existing in the measured vibration data may lead to false or unreliable output result from such model. In this study, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The kriging technique allows one to genuinely quantify the surrogate error, therefore it is chosen as metamodeling technique. Enhanced version of ideal gas molecular movement (EIGMM) algorithm is used as main algorithm for model updating. The developed approach is applied to detect simulated damage in numerical models of 72-bar space truss and 120-bar dome truss. The simulation results show the proposed method can perform well in probability-based damage detection of structures with less computational effort compared to direct finite element model.
[1] S. J. S. Hakim and H. A. Razak, “Modal parameters based structural damage detection using artificial neural networks-a review,” Smart Struct. Syst., vol. 14, no. 2, pp. 159–189, 2014.
[2] W. Fan and P. Qiao, “Vibration-based damage identification methods: a review and comparative study,” Struct. Heal. Monit., vol. 10, no. 1, pp. 83–111, 2011.
[3] 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.
[4] G. G. Wang and S. Shan, “Review of metamodeling techniques in support of engineering design optimization,” J. Mech. Des., vol. 129, no. 4, pp. 370–380, 2007.
[5] H. Fathnejat, P. Torkzadeh, E. Salajegheh, and R. Ghiasi, “Structural damage detection by model updating method based on cascade feed-forward neural network as an efficient approximation mechanism,” Int. J. Optim. Civ. Eng., vol. 4, no. 4, pp. 451–472, 2014.
[6] S. Mahmoudi, F. Trivaudey, and N. Bouhaddi, “Benefits of metamodel-reduction for nonlinear dynamic response analysis of damaged composite structures,” Finite Elem. Anal. Des., vol. 119, pp. 1–14, 2016.
[7] E. Simoen, G. De Roeck, and G. Lombaert, “Dealing with uncertainty in model updating for damage assessment: A review,” Mech. Syst. Signal Process., no. 56, pp. 123–149, 2015.
[8] L. Papadopoulos and E. Garcia, “Structural Damage Identification: A Probabilistic Approach,” AIAA J., vol. 36, no. 11, pp. 2137–2145, 1998.
[9] H. Varaee and M. R. Ghasemi, “Engineering optimization based on ideal gas molecular movement algorithm,” Eng. Comput., pp. 1–23, 2016.
[10] V. Dubourg and B. Sudret, “Reliability-based design optimization using kriging surrogates and subset simulation,” Struct. Multidiscip. Optim., vol. 44, no. 5, pp. 673–690, 2011.
[11] 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.
[12] Q. Xu, E. Wehrle, and H. Baier, “Knowledge-Based Surrogate Modeling in Engineering Design Optimization,” in Surrogate-Based Modeling and Optimization, Springer, pp. 313–336, 2013.
[13] 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.
[14] 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.
[15] A. Kaveh and A. Zolghadr, “An improved CSS for damage detection of truss structures using changes in natural frequencies and mode shapes,” Adv. Eng. Softw., vol. 80, pp. 93–100, 2015.
[16] 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.
[17] M. R. Ghasemi, R. Ghiasi and H. Varaee, “Probability-based Damage Detection of Structures using Model Updating with Enhanced Ideal Gas Molecular Movement Algorithm,” Proceedings of the 19th International Conference on Reliability and Structural Safety (ICRSS 2017), London, United Kingdom (to be published).
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] N. T. Kottegoda and R. Rosso, Probability, Statistics, and Reliability for Civil and Environmental Engineers. The McGraw-Hill Companies, 1997.
[23] 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.
[24] Xia Y, et al. Damage identification of structures with uncertain frequency and mode shape data. Earthquake Engineering and Structural Dynamics, 2002;31(5):1053–66.
[25] R. L. Iman, Latin hypercube sampling. Wiley Online Library, 2008.
[26] B. Dizangian and M. R. Ghasemi, “A fast decoupled reliability-based design optimization of structures using B-spline interpolation curves of structures using B spline interpolation curves,” J. Brazilian Soc. Mech. Sci. Eng., no. September, 2015.
[27] A. Kaveh and S. Talatahari, “Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures,” Comput. Struct., vol. 87, no. 5–6, pp. 267–283, 2009.
[28] S. Mazzoni, F. McKenna, M. H. Scott, and G. L. Fenves, “OpenSees command language manual,” Pacific Earthq. Eng. Res. Cent., 2006.
[1] S. J. S. Hakim and H. A. Razak, “Modal parameters based structural damage detection using artificial neural networks-a review,” Smart Struct. Syst., vol. 14, no. 2, pp. 159–189, 2014.
[2] W. Fan and P. Qiao, “Vibration-based damage identification methods: a review and comparative study,” Struct. Heal. Monit., vol. 10, no. 1, pp. 83–111, 2011.
[3] 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.
[4] G. G. Wang and S. Shan, “Review of metamodeling techniques in support of engineering design optimization,” J. Mech. Des., vol. 129, no. 4, pp. 370–380, 2007.
[5] H. Fathnejat, P. Torkzadeh, E. Salajegheh, and R. Ghiasi, “Structural damage detection by model updating method based on cascade feed-forward neural network as an efficient approximation mechanism,” Int. J. Optim. Civ. Eng., vol. 4, no. 4, pp. 451–472, 2014.
[6] S. Mahmoudi, F. Trivaudey, and N. Bouhaddi, “Benefits of metamodel-reduction for nonlinear dynamic response analysis of damaged composite structures,” Finite Elem. Anal. Des., vol. 119, pp. 1–14, 2016.
[7] E. Simoen, G. De Roeck, and G. Lombaert, “Dealing with uncertainty in model updating for damage assessment: A review,” Mech. Syst. Signal Process., no. 56, pp. 123–149, 2015.
[8] L. Papadopoulos and E. Garcia, “Structural Damage Identification: A Probabilistic Approach,” AIAA J., vol. 36, no. 11, pp. 2137–2145, 1998.
[9] H. Varaee and M. R. Ghasemi, “Engineering optimization based on ideal gas molecular movement algorithm,” Eng. Comput., pp. 1–23, 2016.
[10] V. Dubourg and B. Sudret, “Reliability-based design optimization using kriging surrogates and subset simulation,” Struct. Multidiscip. Optim., vol. 44, no. 5, pp. 673–690, 2011.
[11] 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.
[12] Q. Xu, E. Wehrle, and H. Baier, “Knowledge-Based Surrogate Modeling in Engineering Design Optimization,” in Surrogate-Based Modeling and Optimization, Springer, pp. 313–336, 2013.
[13] 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.
[14] 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.
[15] A. Kaveh and A. Zolghadr, “An improved CSS for damage detection of truss structures using changes in natural frequencies and mode shapes,” Adv. Eng. Softw., vol. 80, pp. 93–100, 2015.
[16] 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.
[17] M. R. Ghasemi, R. Ghiasi and H. Varaee, “Probability-based Damage Detection of Structures using Model Updating with Enhanced Ideal Gas Molecular Movement Algorithm,” Proceedings of the 19th International Conference on Reliability and Structural Safety (ICRSS 2017), London, United Kingdom (to be published).
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] N. T. Kottegoda and R. Rosso, Probability, Statistics, and Reliability for Civil and Environmental Engineers. The McGraw-Hill Companies, 1997.
[23] 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.
[24] Xia Y, et al. Damage identification of structures with uncertain frequency and mode shape data. Earthquake Engineering and Structural Dynamics, 2002;31(5):1053–66.
[25] R. L. Iman, Latin hypercube sampling. Wiley Online Library, 2008.
[26] B. Dizangian and M. R. Ghasemi, “A fast decoupled reliability-based design optimization of structures using B-spline interpolation curves of structures using B spline interpolation curves,” J. Brazilian Soc. Mech. Sci. Eng., no. September, 2015.
[27] A. Kaveh and S. Talatahari, “Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures,” Comput. Struct., vol. 87, no. 5–6, pp. 267–283, 2009.
[28] S. Mazzoni, F. McKenna, M. H. Scott, and G. L. Fenves, “OpenSees command language manual,” Pacific Earthq. Eng. Res. Cent., 2006.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:75107", author = "M. R. Ghasemi and R. Ghiasi and H. Varaee", title = "Probability-Based Damage Detection of Structures Using Kriging Surrogates and Enhanced Ideal Gas Molecular Movement Algorithm", abstract = "Surrogate model has received increasing attention for use in detecting damage of structures based on vibration modal parameters. However, uncertainties existing in the measured vibration data may lead to false or unreliable output result from such model. In this study, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The kriging technique allows one to genuinely quantify the surrogate error, therefore it is chosen as metamodeling technique. Enhanced version of ideal gas molecular movement (EIGMM) algorithm is used as main algorithm for model updating. The developed approach is applied to detect simulated damage in numerical models of 72-bar space truss and 120-bar dome truss. The simulation results show the proposed method can perform well in probability-based damage detection of structures with less computational effort compared to direct finite element model.
", keywords = "Enhanced ideal gas molecular movement, Kriging, probability-based damage detection, probability of damage existence, surrogate modeling, uncertainty quantification.", volume = "11", number = "4", pages = "733-9", }
