Probability-Based Damage Detection of Structures Using Model Updating with Enhanced Ideal Gas Molecular Movement Algorithm

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.

Authors:

• M. R. Ghasemi
• R. Ghiasi
• H. Varaee

Keywords:

• Enhanced ideal gas molecular movement
• ideal gas molecular movement
• model updating method
• probability-based damage detection
• uncertainty quantification.

References:

[1] C. Boller, F.-K. Chang, and Y. Fujino, Encyclopedia of structural health
monitoring. John Wiley & Sons, 2009.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] H. Varaee and M. R. Ghasemi, “Engineering optimization based on
ideal gas molecular movement algorithm,” Eng. Comput., pp. 1–23,
2016.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] N. T. Kottegoda and R. Rosso, Probability, Statistics, and Reliability
for Civil and Environmental Engineers. The McGraw-Hill Companies,
1997.
[16] 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.
[17] 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.