Identification of Damage and Model Updating Using Residual Force Modal Analysis and Optimization Algorithms

Document Type : Original Article

Authors

1 Department of Civil Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.

2 Department of Civil Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.

10.22091/cer.2025.11502.1584

Abstract

Currently, detecting structural failures is considered a highly vital and essential method in all branches of engineering. This method assists in improving the quality of life and safety by providing the necessary conditions to increase the useful life of structures and prevent the spread of damage. One of the efficient and widely used methods for detecting failures is to utilize their effects on the dynamic or static responses of structures. This method can serve as a key indicator for detecting and analyzing failures and vulnerable points in structures. In this article, a model updating approach has been employed to detect and determine possible damaged locations in structures. Additionally, a new objective function has been proposed to evaluate the extent of damage based on the combination of residual modal forces and natural frequencies. This objective function, using equilibrium algorithms, differential evolution, cluster analysis, and ant colony optimization, determines the location and severity of existing damages. To assess the accuracy and validity of the presented theory, two numerical examples have been conducted, involving two three-dimensional footings with 25 and 72 elements. These examples cover dual and quadruple damage scenarios in the elements, demonstrating that this theory is capable of accurately detecting the locations and severity of damages in the presence of noisy data and effectively contributing to the correction and improvement of the quality and safety of structures.

Keywords

Main Subjects

References

[1] Soyoz S. Model updating techniques for structures under seismic excitation. InSeismic Structural Health Monitoring: From Theory to Successful Applications 2019 Apr 25; 199-216. doi: 10.1007/978-3-030-13976-6_8
[2] Liu H, Xin K, Qi Q. Study of structural damage detection with multi-objective function genetic algorithms. Procedia Engineering. 2011; 12: 80-86. doi: 10.1016/j.proeng.2011.05.014
[3] Levin R, Lieven N. Dynamic finite element model updating using simulated annealing and genetic algorithms. Mechanical systems and signal processing. 1998; 12(1): 91-120. doi: 10.1006/mssp.1996.0136
[4] Carden EP, Fanning P. Vibration based condition monitoring: a review. Structural health monitoring. 2004; 3(4): 355-377. doi: 10.1177/1475921704047500
[5] Beck JL, Katafygiotis LS. Updating models and their uncertainties. I: Bayesian statistical framework. Journal of Engineering Mechanics. 1998; 124(4): 455-461. doi: 10.1061/(ASCE)0733-9399(1998)124:4(455)
[6] Ghanem R, Shinozuka M. Structural-system identification. I: Theory. Journal of Engineering Mechanics. 1995; 121(2): 255-264. doi: 10.1061/(ASCE)0733-9399(1995)121:2(255)
[7] Brownjohn J, Pan T-C, Deng X. Correlating dynamic characteristics from field measurements and numerical analysis of a high‐rise building. Earthquake Engineering & Structural Dynamics. 2000; 29(4): 523-543. doi: 10.1002/(SICI)1096-9845(200004)29:4%3C523::AID-EQE920%3E3.0.CO;2-L
[8] Caetano E, Cunha A, Gattulli V, Lepidi M. Cable–deck dynamic interactions at the International Guadiana Bridge: On‐site measurements and finite element modelling. Structural Control and Health Monitoring: The Official Journal of the International Association for Structural Control and Monitoring and of the European Association for the Control of Structures. 2008; 15(3): 237-264. doi: 10.1002/stc.241
[9] Boroschek RL, Yáñez FV. Experimental verification of basic analytical assumptions used in the analysis of structural wall buildings. Engineering Structures. 2000; 22(6): 657-669. doi: 10.1016/S0141-0296(99)00007-3
[10] Yu E, Taciroglu E, Wallace JW. Parameter identification of framed structures using an improved finite element model‐updating method-Part I: formulation and verification. Earthquake Engineering & Structural Dynamics. 2007; 36(5): 619-639. doi: 10.1002/eqe.646
[11] Gentile C, Saisi A. Ambient vibration testing of historic masonry towers for structural identification and damage assessment. Construction and building materials. 2007; 21(6): 1311-1321. doi: 10.1016/j.conbuildmat.2006.01.007
[12] Soyoz S, Feng MQ. Instantaneous damage detection of bridge structures and experimental verification. Structural Control and Health Monitoring: The Official Journal of the International Association for Structural Control and Monitoring and of the European Association for the Control of Structures. 2008; 15(7): 958-973. doi: 10.1002/stc.229
[13] Weng J-H, Loh C-H, Yang JN. Experimental study of damage detection by data-driven subspace identification and finite-element model updating. Journal of structural engineering. 2009; 135(12): 1533-1544. doi: 10.1061/(ASCE)ST.1943-541X.0000079
[14] Belleri A, Moaveni B, Restrepo JI. Damage assessment through structural identification of a three‐story large‐scale precast concrete structure. Earthquake engineering & structural dynamics. 2014 Jan; 43(1): 61-76. doi: 10.1002/eqe.2332
[15] Bassoli E, Vincenzi L, D'Altri AM, de Miranda S, Forghieri M, Castellazzi G. Ambient vibration‐based finite element model updating of an earthquake‐damaged masonry tower. Structural Control and Health Monitoring. 2018; 25(5): e2150. doi: 10.1002/stc.2150
[16] Ubertini F, Cavalagli N, Kita A, Comanducci G. Assessment of a monumental masonry bell-tower after 2016 Central Italy seismic sequence by long-term SHM. Bulletin of Earthquake Engineering. 2018 Feb;16(2):775-801. doi: 10.1007/s10518-017-0222-7
[17] Bagherkhani A, Baghlani A. Enhancing the curvature mode shape method for structural damage severity estimation by means of the distributed genetic algorithm. Engineering Optimization. 2021; 53(4): 683-701 .doi: 10.1080/0305215X.2020.1746294
[18] Dessena G, Ignatyev DI, Whidborne JF, Fragonara LZ. A global–local meta-modelling technique for model updating. Computer Methods in Applied Mechanics and Engineering. 2024; 418: 116511. doi: 10.1016/j.cma.2023.116511
[19] Yang Q. A numerical technique for structural damage detection. Applied Mathematics and Computation. 2009; 215(7): 2775-2780. doi: 10.1016/j.amc.2009.08.039
[20] Ge M, Lui EM. Structural damage identification using system dynamic properties. Computers & structures. 2005; 83(27): 2185-2196. doi: 10.1016/j.compstruc.2005.05.002
[21] Nazari-Heris M, Mohammadi-Ivatloo B, Gharehpetian G. A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives. Renewable and Sustainable Energy Reviews. 2018; 81: 2128-2143. doi: 10.1016/j.rser.2017.06.024
[22] Shao L, Yan R, Li X, Liu Y. From heuristic optimization to dictionary learning: A review and comprehensive comparison of image denoising algorithms. IEEE transactions on cybernetics. 2013; 44(7): 1001-1013 .doi: 10.1109/TCYB.2013.2278548
[23] Beheshti Z, Shamsuddin SMH. A review of population-based meta-heuristic algorithms. Int j adv soft comput appl. 2013; 5(1): 1-35.
[24] Gilli M, Winker P. A review of heuristic optimization methods in econometrics. Swiss Finance Institute Research Paper. 2008; 8-12.
[25] Bekdaş G, Nigdeli SM, Kayabekir AE, Yang X-S. Optimization in civil engineering and metaheuristic algorithms: a review of state-of-the-art developments. Computational intelligence, optimization and inverse problems with applications in engineering. 2019; 111-137. doi: 10.1007/978-3-319-96433-1_6
[26] Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S. Equilibrium optimizer: A novel optimization algorithm. Knowledge-based systems. 2020; 191: 105190. doi: 10.1016/j.knosys.2019.105190
[27] Price K. Differential Evolution: a Practical Approach to Global Optimization: Springer Science & Business Media; 2006.
[28] Mohamed A-AA, Mohamed YS, El-Gaafary AA, Hemeida AM. Optimal power flow using moth swarm algorithm. Electric Power Systems Research. 2017; 142: 190-206. doi: 10.1016/j.epsr.2016.09.025
[29] Mirjalili S. The ant lion optimizer. Advances in engineering software. 2015; 83: 80-98. doi: 10.1016/j.advengsoft.2015.01.010
 
Send comment about this article
CAPTCHA Image

Files

Share

How to cite

Statistics