A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF
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 detectio...
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| Format: | Article |
| Language: | English |
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Académie des sciences
2024-03-01
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| Series: | Comptes Rendus. Mécanique |
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| Online Access: | https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.241/ |
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| author | Zenzen, Roumaissa Ayadi, Ayoub Benaissa, Brahim Belaidi, Idir Sukic, Enes Khatir, Tawfiq |
| author_facet | Zenzen, Roumaissa Ayadi, Ayoub Benaissa, Brahim Belaidi, Idir Sukic, Enes Khatir, Tawfiq |
| author_sort | Zenzen, Roumaissa |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-06d02472c5fc4d1e8bed23521b4386de |
| institution | Kabale University |
| issn | 1873-7234 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Académie des sciences |
| record_format | Article |
| series | Comptes Rendus. Mécanique |
| spelling | doaj-art-06d02472c5fc4d1e8bed23521b4386de2025-02-07T13:48:46ZengAcadémie des sciencesComptes Rendus. Mécanique1873-72342024-03-01352G1557010.5802/crmeca.24110.5802/crmeca.241A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBFZenzen, Roumaissa0https://orcid.org/0009-0007-8921-9947Ayadi, Ayoub1Benaissa, Brahim2https://orcid.org/0000-0002-9472-9331Belaidi, Idir3https://orcid.org/0000-0003-3463-0580Sukic, Enes4https://orcid.org/0000-0002-0991-5480Khatir, Tawfiq5https://orcid.org/0009-0001-9553-4608LMT Laboratory, Faculty of Sciences and Technology, University of Jijel, Jijel, AlgeriaUniversity of Biskra, Laboratoire de Génie Energétique et Matériaux, LGEM, Faculty of Sciences and Technology, Biskra, 07000, AlgeriaDesign Engineering Laboratory, Toyota Technological Institute, Nagoya, JapanLEMI Laboratory, Department of Mechanical Engineering, University M’hamed Bougara Boumerdes, 35000 Boumerdes, AlgeriaFaculty of Information Technology and Engineering - FITI, University Union - Nikola Tesla, 11070 Belgrade, SerbiaArtificial Intelligence Laboratory for Mechanical and Civil Structures, and Soil, Institute of Technology, University Center of Naama, 45000 Naama, P.O.B. 66, AlgeriaIn 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.https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.241/Crack identificationModel reductionExperimental modal analysisInverse analysisYUKI algorithm |
| spellingShingle | Zenzen, Roumaissa Ayadi, Ayoub Benaissa, Brahim Belaidi, Idir Sukic, Enes Khatir, Tawfiq 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 Crack identification Model reduction Experimental modal analysis Inverse analysis YUKI algorithm |
| title | A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF |
| title_full | A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF |
| title_fullStr | A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF |
| title_full_unstemmed | A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF |
| title_short | A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF |
| title_sort | comparative analysis for crack identification in structural health monitoring a focus on experimental crack length prediction with yuki and pod rbf |
| topic | Crack identification Model reduction Experimental modal analysis Inverse analysis YUKI algorithm |
| url | https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.241/ |
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