Structural Damage Detection Using Novel Objective Function and Hybrid Metaheuristic Algorithms

Sabzevari, Soheil; Naderpour, Hosein; Gholhaki, Majid

doi:10.22115/scce.2025.2167

Structural Damage Detection Using Novel Objective Function and Hybrid Metaheuristic Algorithms

Document Type : Regular Article

Authors

Soheil Sabzevari ¹

Hosein Naderpour ²

Majid Gholhaki ²

¹ Ph.D. Candidate, Faculty of Civil Engineering, Semnan University, Semnan, Iran

² Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran

10.22115/scce.2025.2167

Abstract

Structural damage detection is vital for ensuring the safety, integrity, and serviceability of structures, which are inherently susceptible to progressive deterioration over time. This study presents an advanced model updating framework enhanced with metaheuristic optimization algorithms for accurate and robust identification of structural damage. A novel objective function was formulated by integrating three critical dynamic features: natural frequencies, mode shapes, and modal strain energy. To validate the effectiveness and robustness of the proposed approach, five metaheuristic algorithms—GOA, WOA, MRFO, SBOA, and AOA—were employed and their performance evaluated across two predefined damage scenarios under three levels of simulated noise (0%, 3%, and 10%). Comparative results indicated that while MRFO and SBOA achieved higher accuracy and noise resistance, GOA demonstrated faster convergence rates. To overcome limitations observed in individual algorithms, a novel hybrid optimization algorithm, MSOA, was developed. MSOA effectively balanced exploration and exploitation tendencies, improved convergence behavior, and reduced the risk of premature convergence, particularly under noisy conditions. The results demonstrate that the proposed model updating framework, which integrates a novel objective function with the MSOA algorithm, provides high accuracy and robustness for structural damage detection in frames, even in the presence of measurement uncertainty.

Highlights

A novel objective function and a hybrid metaheuristic (MSOA) are proposed for accurate detection of damage severity and location in moment-resisting frames
Novel objective function integrating: natural frequencies, mode shapes, modal strain energy
Comparison with five metaheuristic algorithms: GOA, WOA, MRFO, SBOA, AOA
Developed hybrid MSOA algorithm with adaptive switching mechanism

Keywords

Structural damage detection

Model updating

Novel objective function

Optimization algorithm

Hybrid algorithms

Structural health monitoring

Subjects

Structural Optimization

[1] Afshar A, Nouri G, Ghazvineh S, Hosseini Lavassani SH. Machine-Learning Applications in Structural Response Prediction: A Review. Pract Period Struct Des Constr 2024;29:3124002. https://doi.org/10.1061/PPSCFX.SCENG-1292.

[2] Azimi M, Eslamlou A, Pekcan G. Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review. Sensors 2020;20:2778. https://doi.org/10.3390/s20102778.

[3] Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Softw 2014;69:46–61.

[4] Mirjalili S. The Ant Lion Optimizer. Adv Eng Softw 2015;83:80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010.

[5] Yamany W, Fawzy M, Tharwat A, Hassanien AE. Moth-flame optimization for training Multi-Layer Perceptrons. 2015 11th Int. Comput. Eng. Conf., IEEE; 2015, p. 267–72. https://doi.org/10.1109/ICENCO.2015.7416360.

[6] Mirjalili S, Lewis A. The Whale Optimization Algorithm. Adv Eng Softw 2016;95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008.

[7] Samareh Moosavi SH, Khatibi Bardsiri V. Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation. Eng Appl Artif Intell 2017;60:1–15. https://doi.org/10.1016/j.engappai.2017.01.006.

[8] Fu Y, Liu D, Chen J, He L. Addax Optimization Algorithm: A Novel Nature-Inspired Optimizer for Solving Engineering Applications. Int J Intell Eng Syst 2024;17:732–43. https://doi.org/10.22266/ijies2024.0630.57.

[9] Zhao W, Zhang Z, Wang L. Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intell 2020;87:103300. https://doi.org/10.1016/j.engappai.2019.103300.

[10] Fu Y, Liu D, Chen J, He L. Secretary bird optimization algorithm: a new metaheuristic for solving global optimization problems. Artif Intell Rev 2024;57:123. https://doi.org/10.1007/s10462-024-10729-y.

[11] Hamadneh T, Kaabneh K, Alssayed O, Eguchi K, Gochhait S, Leonova I, et al. Addax Optimization Algorithm: A Novel Nature-Inspired Optimizer for Solving Engineering Applications. Int J Intell Eng Syst 2024;17:732–43. https://doi.org/10.22266/ijies2024.0630.57.

[12] Liu G, Zhai Y, Leng D, Tian X, Mu W. Research on structural damage detection of offshore platforms based on grouping modal strain energy. Ocean Eng 2017;140:43–9. https://doi.org/10.1016/j.oceaneng.2017.05.021.

[13] Liu G, Zhai Y, Leng D, Tian X, Mu W. Research on structural damage detection of offshore platforms based on grouping modal strain energy. Ocean Eng 2017;140:43–9. https://doi.org/10.1016/j.oceaneng.2017.05.021.

[14] Li XY, Wang LX, Law SS, Nie ZH. Covariance of dynamic strain responses for structural damage detection. Mech Syst Signal Process 2017;95:90–105. https://doi.org/10.1016/j.ymssp.2017.03.020.

[15] Tan ZX, Thambiratnam DP, Chan THT, Abdul Razak H. Detecting damage in steel beams using modal strain energy based damage index and Artificial Neural Network. Eng Fail Anal 2017;79:253–62. https://doi.org/10.1016/j.engfailanal.2017.04.035.

[16] Nabavi S, Gholampour S, Haji MS. Damage detection in frame elements using Grasshopper Optimization Algorithm (GOA) and time-domain responses of the structure. Evol Syst 2022;13:307–18. https://doi.org/10.1007/s12530-021-09389-y.

[17] Nouri Y, Shahabian F, Shariatmadar H, Entezami A. Structural Damage Detection in the Wooden Bridge Using the Fourier Decomposition, Time Series Modeling and Machine Learning Methods. J Soft Comput Civ Eng 2024;8:83–101. https://doi.org/10.22115/scce.2023.401971.1669.

[18] Soleimani Nezhad S, Khademian F, Naderpour H, Kalantari SM, Fakharian P. Signal processing-based damage detection of steel braced frame subjected to consequent excitations. Innov Infrastruct Solut 2024;9:454. https://doi.org/10.1007/s41062-024-01762-5.

[19] Fakharian P, Naderpour H. Damage Severity Quantification Using Wavelet Packet Transform and Peak Picking Method. Pract Period Struct Des Constr 2022;27. https://doi.org/10.1061/(ASCE)SC.1943-5576.0000639.

[20] Du G-F, Bie X-M, Li Z, Guan W-Q. Study on constitutive model of shear performance in panel zone of connections composed of CFSSTCs and steel-concrete composite beams with external diaphragms. Eng Struct 2018;155:178–91. https://doi.org/10.1016/j.engstruct.2017.11.024.

[21] Khatir S, Abdel Wahab M. A computational approach for crack identification in plate structures using XFEM, XIGA, PSO and Jaya algorithm. Theor Appl Fract Mech 2019;103:102240. https://doi.org/10.1016/j.tafmec.2019.102240.

[22] Mehrian SZ, Amrei SAR, Maniat M, Nowruzpour SM. Structural health monitoring using optimising algorithms based on flexibility matrix approach and combination of natural frequencies and mode shapes. Int J Struct Eng 2016;7:398. https://doi.org/10.1504/IJSTRUCTE.2016.079287.

[23] Hoseini Vaez SR, Fallah N. Damage Detection of Thin Plates Using GA-PSO Algorithm Based on Modal Data. Arab J Sci Eng 2017;42:1251–63. https://doi.org/10.1007/s13369-016-2398-6.

[24] Rokhsati M, Mohamadi Dehcheshmeh M, Ghodrati Amiri G, Raissi Dehkordi M. Structural Damage Identification Using Novel Objective Functions and Metaheuristic Algorithms. J Soft Comput Civ Eng 2025;9:95–124. https://doi.org/10.22115/scce.2024.465878.1908.

[25] Huang M, Cheng X, Lei Y. Structural damage identification based on substructure method and improved whale optimization algorithm. J Civ Struct Heal Monit 2021;11:351–80. https://doi.org/10.1007/s13349-020-00456-7.

[26] Nouri Y, Shariatmadar H, Shahabian F. Nonlinearity detection using new signal analysis methods for global health monitoring. Sci Iran 2023;30:845–59. https://doi.org/10.24200/sci.2022.58196.5610.

[27] Sabzevari S, Naderpour H, Gholhaki M. Damage Identification in Moment Frames Using Mode Shape Parameters and IMRFO Algorithm. Civ Infrastruct Res 2025. https://doi.org/10.22091/cer.2025.13741.1648.

[28] Heravi MA, Tavakkoli SM, Entezami A. Structural health monitoring by probability density function of autoregressive-based damage features and fast distance correlation method. J Vib Control 2022;28:2786–802. https://doi.org/10.1177/10775463211020198.

[29] Heravi MA, Naderpour H, Soleimani-Babakamali MH. Unbiased Normalized Ensemble Methodology for Zero‐Shot Structural Damage Detection Using Manifold Learning and Reconstruction Error From Variational Autoencoder. Struct Control Heal Monit 2025;2025:8921708. https://doi.org/10.1155/stc/8921708.

[30] Zare Hosseinzadeh A, Ghodrati Amiri G, Jafarian Abyaneh M, Seyed Razzaghi SA, Ghadimi Hamzehkolaei A. Baseline updating method for structural damage identification using modal residual force and grey wolf optimization. Eng Optim 2020;52:549–66. https://doi.org/10.1080/0305215X.2019.1593400.

[31] Chen Z, Yu L. A new structural damage detection strategy of hybrid PSO with Monte Carlo simulations and experimental verifications. Measurement 2018;122:658–69. https://doi.org/10.1016/j.measurement.2018.01.068.

[32] Nguyen-Ngoc L, Tran NH, Bui-Tien T, Mai-Duc A, Abdel Wahab M, X Nguyen H, et al. Damage detection in structures using particle swarm optimization combined with artificial neural network. Smart Struct Syst 2021;28:1–12. https://doi.org/10.12989/sss.2021.28.1.001.

[33] Chen Z, Zhang K, Chan THT, Li X, Zhao S. A Novel Hybrid Whale-Chimp Optimization Algorithm for Structural Damage Detection. Appl Sci 2022;12:9036. https://doi.org/10.3390/app12189036.

[34] Zhang G, Wan C, Yang Z, Xie L, Xue S. Damage detection of civil structures based on hybrid optimization algorithm and combined correlation function of heterogeneous responses. Measurement 2025;246:116678. https://doi.org/10.1016/j.measurement.2025.116678.

[35] Logan DL. A first course in the finite element method. vol. 4. Thomson; 2011.

[36] Chopra AK. Modal Analysis of Linear Dynamic Systems: Physical Interpretation. J Struct Eng 1996;122:517–27. https://doi.org/10.1061/(ASCE)0733-9445(1996)122:5(517).