In recent years, substantial investments in structural construction underscore the paramount importance of ensuring structural integrity for safety and dependability. Structural Health Monitoring (SHM) has emerged as a pivotal tool for assessing structural health, with an emphasis on damage detection, localisation, and quantification, particularly through vibration-based methods that exploit variations in modal properties as precursors to structural damage. This study presents an innovative methodology that synergistically combines Proper Orthogonal Decomposition and Radial Basis Function interpolation for predicting structural responses based on crack parameters. Additionally, the YUKI algorithm, leveraging population clustering for optimisation, is introduced. The approach is rigorously assessed through experimental analysis of two distinct beams (Beam I and Beam II) exhibiting varying crack depths. The results demonstrate the effectiveness of the POD-RBF-YUKI approach, indicating a notable level of accuracy and consistency. Comparative evaluations with conventional optimisation algorithms, namely Cuckoo, Bat, and Particle Swarm Optimisation, reveal similar Mean Percentage Error values but with increased result variability, whereas Deep Artificial Neural Network models with varied hidden layer sizes.
Ces dernières années, des investissements substantiels dans la construction structurelle ont mis en évidence l’importance primordiale de l’intégrité structurelle pour la sécurité et la fiabilité. La surveillance de la santé des structures (SHM) est devenue un outil essentiel pour évaluer la santé des structures, en mettant l’accent sur la détection, la localisation et la quantification des dommages, en particulier grâce à des méthodes basées sur les vibrations qui exploitent les variations des propriétés modales en tant que précurseurs des dommages structurels. Cette étude présente une méthodologie innovante qui combine de manière synergique la décomposition orthogonale appropriée (POD) et l’interpolation de la fonction de base radiale (RBF) pour prédire les réponses structurelles basées sur les paramètres des fissures. En outre, l’algorithme YUKI, qui tire parti du regroupement de populations pour l’optimisation, est présenté. L’approche est rigoureusement évaluée par l’analyse expérimentale de deux poutres distinctes (poutre I et poutre II) présentant différentes profondeurs de fissures. Les résultats démontrent l’efficacité de l’approche POD-RBF-YUKI, indiquant un niveau notable de précision et de cohérence. Les évaluations comparatives avec les algorithmes d’optimisation conventionnels, à savoir Cuckoo, Bat et Particle Swarm Optimisation, révèlent des valeurs d’erreur moyenne similaires mais avec une variabilité accrue des résultats, tandis que les modèles de réseaux neuronaux artificiels profonds (ANN) avec des tailles de couches cachées variées.
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Mots-clés : Identification des fissures, Réduction du modèle, Analyse modale expérimentale, Analyse inverse, Algorithme YUKI
Roumaissa Zenzen 1; Ayoub Ayadi 2; Brahim Benaissa 3; Idir Belaidi 4; Enes Sukic 5; Tawfiq Khatir 6

@article{CRMECA_2024__352_G1_55_0, author = {Roumaissa Zenzen and Ayoub Ayadi and Brahim Benaissa and Idir Belaidi and Enes Sukic and Tawfiq Khatir}, title = {A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with {YUKI} and {POD-RBF}}, journal = {Comptes Rendus. M\'ecanique}, pages = {55--70}, publisher = {Acad\'emie des sciences, Paris}, volume = {352}, year = {2024}, doi = {10.5802/crmeca.241}, language = {en}, }
TY - JOUR AU - Roumaissa Zenzen AU - Ayoub Ayadi AU - Brahim Benaissa AU - Idir Belaidi AU - Enes Sukic AU - Tawfiq Khatir TI - A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF JO - Comptes Rendus. Mécanique PY - 2024 SP - 55 EP - 70 VL - 352 PB - Académie des sciences, Paris DO - 10.5802/crmeca.241 LA - en ID - CRMECA_2024__352_G1_55_0 ER -
%0 Journal Article %A Roumaissa Zenzen %A Ayoub Ayadi %A Brahim Benaissa %A Idir Belaidi %A Enes Sukic %A Tawfiq Khatir %T A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF %J Comptes Rendus. Mécanique %D 2024 %P 55-70 %V 352 %I Académie des sciences, Paris %R 10.5802/crmeca.241 %G en %F CRMECA_2024__352_G1_55_0
Roumaissa Zenzen; Ayoub Ayadi; Brahim Benaissa; Idir Belaidi; Enes Sukic; Tawfiq Khatir. A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF. Comptes Rendus. Mécanique, Volume 352 (2024), pp. 55-70. doi : 10.5802/crmeca.241. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.241/
[1] Vibrational based inspection of civil engineering structures, PhD thesis, Dept. of Building Technology and Structural Engineering, Aalborg University (1993)
[2] Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review (1996) (Technical report) | DOI
[3] The state of the art of data science and engineering in structural health monitoring, Engineering, Volume 5 (2019), pp. 234-242 | DOI
[4] State-of-the-art in structural health monitoring of large and complex civil infrastructures, J. Civ. Struct. Health Monit., Volume 6 (2016), pp. 3-16 | DOI
[5] Structural health monitoring: State of the art and perspectives, JOM, Volume 64 (2012), pp. 789-792 | DOI
[6] A review of structural health monitoring literature: 1996–2001, Los Alamos National Laboratory, Los Alamos, NM, USA (2003) (LA-13976-MS) (Technical report)
[7] Novel approach-based sparsity for damage localization in functionally graded material, Buildings, Volume 13 (2023), 1768 | DOI
[8] Detecting damages in metallic beam structures using a novel wavelet selection criterion, J. Sound Vib., Volume 578 (2024), 118297 | DOI
[9] A novel machine-learning based on the global search techniques using vectorized data for damage detection in structures, Int. J. Eng. Sci., Volume 157 (2020), 103376 | DOI | MR | Zbl
[10] Optimal axial-probe design for foucault-current tomography: a global optimization approach based on linear sampling method, Energies, Volume 16 (2023), 2448 | DOI
[11] Experimental sensitivity analysis of sensor placement based on virtual springs and damage quantification in CFRP composite, J. Mater. Eng. Struct., Volume 9 (2022), pp. 207-220
[12] Vibration-based model-dependent damage (delamination) identification and health monitoring for composite structures—a review, J. Sound Vib., Volume 230 (2000), pp. 357-378 | DOI
[13] Enhanced ANN predictive model for composite pipes subjected to low-velocity impact loads, Buildings, Volume 13 (2023), 973 | DOI
[14] Vibration-based damage assessment in truss structures using local frequency change ratio indicator combined with metaheuristic optimization algorithms BT, Proceedings of the International Conference of Steel and Composite for Engineering Structures (R. Capozucca; S. Khatir; G. Milani, eds.), Springer International Publishing, Cham, 2023, pp. 171-185 | DOI
[15] Review on vibration-based structural health monitoring techniques and technical codes, Symmetry (Basel), Volume 13 (2021), 1998
[16] Vibration-based damage identification methods: a review and comparative study, Struct. Health Monit., Volume 10 (2011), pp. 83-111 | DOI
[17] A vibration technique for non-destructively assessing the integrity of structures, J. Mech. Eng. Sci., Volume 20 (1978), pp. 93-100 | DOI
[18] Detection of structural damage through changes in frequency: a review, Eng. Struct., Volume 19 (1997), pp. 718-723 | DOI
[19] Vibration based condition monitoring: a review, Struct. Health Monit., Volume 3 (2004), pp. 355-377 | DOI
[20] A state-of-the-art review on frf-based structural damage detection: development in last two decades and way forward, Int. J. Struct. Stab. Dyn., Volume 22 (2022), 2230001
[21] Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019, J. Sound Vib., Volume 491 (2021), 115741
[22] Machine-learning methods for computational science and engineering, Computation, Volume 8 (2020), 15 | DOI
[23] Neurocomputing in civil infrastructure, Sci. Iran., Volume 23 (2016), pp. 2417-2428
[24] Machine learning in structural engineering, Sci. Iran., Volume 27 (2020), pp. 2645-2656
[25] Artificial neural network and yuki algorithm for notch depth prediction in X70 steel specimens, Theor. Appl. Fract. Mech., Volume 129 (2023), 104227
[26] A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications, Mech. Syst. Signal Process., Volume 147 (2021), 107077 | DOI
[27] Damage detection using artificial neural network with consideration of uncertainties, Engng Struct., Volume 29 (2007), pp. 2806-2815 | DOI
[28] Neural networks-based damage detection for bridges considering errors in baseline finite element models, J. Sound Vib., Volume 280 (2005), pp. 555-578 | DOI
[29] Damage detection of truss bridge joints using artificial neural networks, Expert Syst. Appl., Volume 35 (2008), pp. 1122-1131 | DOI
[30] An efficient hybrid TLBO-PSO-ANN for fast damage identification in steel beam structures using IGA, Smart Struct. Syst., Volume 25 (2020), pp. 605-617
[31] An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm, Eng. Struct., Volume 199 (2019), 109637 | DOI
[32] A damage identification technique for beam-like and truss structures based on FRF and Bat Algorithm, C. R. Méc., Volume 346 (2018), pp. 1253-1266 | DOI
[33] A modified transmissibility indicator and Artificial Neural Network for damage identification and quantification in laminated composite structures, Compos. Struct., Volume 248 (2020), 112497 | DOI
[34] Evaluating pod-based unsupervised damage identification using controlled damage propagation of out-of-service bridges, Eng. Struct., Volume 286 (2023), 116096 | DOI
[35] Nonlinear dynamic analysis of a simply supported beam with breathing crack using proper orthogonal decomposition based reduced-order modeling, Advances in Rotor Dynamics, Control, and Structural Health Monitoring: Select Proceedings of ICOVP 2017, Springer, Germany, 2020, pp. 315-325 | DOI
[36] Proper orthogonal decomposition based algorithm for detecting damage location and severity in composite beams, Mech. Syst. Signal Process., Volume 25 (2011), pp. 1062-1072 | DOI
[37] Damage detection in structural systems utilizing artificial neural networks and proper orthogonal decomposition, Struct. Control Health Monit., Volume 26 (2019), e2288 | DOI
[38] A new hybrid PSO-YUKI for double cracks identification in CFRP cantilever beam, Compos. Struct., Volume 311 (2023), 116803 | DOI
[39] Crack identification using model reduction based on proper orthogonal decomposition coupled with radial basis functions, Struct. Multidiscip. Optim., Volume 54 (2016), pp. 265-274 | DOI
[40] YUKI Algorithm and POD-RBF for Elastostatic and dynamic crack identification, J. Comput. Sci., Volume 55 (2021), 101451 | DOI
[41] Damage identification in steel plate using FRF and inverse analysis, Frat. Integr. Strutt. Struct. Integr., Volume 58 (2021), pp. 416-433
[42] Damage identification in frame structure based on inverse analysis, Proceedings of the 2nd International Conference on Structural Damage Modelling and Assessment: SDMA 2021, 4–5 August, Ghent University, Belgium, Springer, Germany, 2022, pp. 197-211 | DOI
[43] Bat algorithm: a novel approach for global engineering optimization, Eng. Comput., Volume 29 (2012), pp. 464-483 | DOI
[44] Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems, Eng. Comput., Volume 29 (2013), pp. 17-35 | DOI
[45] Particle swarm optimization, Proceedings of ICNN’95-International Conference on Neural Networks, Volume 4, IEEE, USA, 1995, pp. 1942-1948 | DOI
[46] Crack prediction in beam-like structure using ANN based on frequency analysis, Frat. Integr. Strutt., Volume 16 (2022), pp. 18-34 | DOI
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