An Efficient Computational System For Defect Prediction through Neural Network And Bio-inspired Algorithms

##plugins.themes.academic_pro.article.main##

Abdelwahhab KHATIR
Abdelmoumin Oulad Brahim
Erica MAGAGNINI

Abstract

Detecting and locating damage is essential in maintaining structural integrity. While Artificial Neural Networks (ANNs) are effective for this purpose, their performance can be significantly improved through advanced optimization techniques. This study introduces a novel approach using the Grasshopper Optimization Algorithm (GOA) to enhance ANN capabilities for predicting defect aluminum plates. The methodology begins by deriving input parameters from natural frequencies, with defect locations as the output. A Finite Element Model (FEM) is used to simulate data by varying defect locations, creating a comprehensive dataset. To validate this approach, experimental data from vibration analyses of plates with different defect locations is collected. We then compare the performance of our GOA-optimized ANN against other metaheuristic algorithms, such as Cuckoo Search Algorithm (CSA), Bat Algorithm (BA), and Firefly Algorithm (FA). Notably, CSA's performance is slightly close to GOA. The results show that our GOA-based method outperforms these traditional algorithms, demonstrating superior accuracy in damage prediction. This advancement holds significant potential for applications in structural integrity monitoring and maintenance.

##plugins.themes.academic_pro.article.details##

How to Cite
KHATIR, A., Oulad Brahim, A., & MAGAGNINI, E. (2024). An Efficient Computational System For Defect Prediction through Neural Network And Bio-inspired Algorithms. HCMCOU Journal of Science – Advances in Computational Structures, 14(2). https://doi.org/10.46223/HCMCOUJS.acs.en.14.2.61.2024

References

  1. Achouri, F., Khatir, A., Smahi, Z., Capozucca, R., & Ouled Brahim, A. (2023). Structural health monitoring of beam model based on swarm intelligence-based algorithms and neural networks employing FRF. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 45(12), 621.
  2. Azimi, M., & Pekcan, G. (2020). Structural health monitoring using extremely compressed data through deep learning. Computer-Aided Civil and Infrastructure Engineering, 35(6), 597-614. doi:https://doi.org/10.1111/mice.12517
  3. Bao, Y., & Li, H. (2020). Machine learning paradigm for structural health monitoring. Structural Health Monitoring, 20(4), 1353-1372. doi:10.1177/1475921720972416
  4. Chen, J., Wang, H., Salemi, M., & Balaguru, P. N. (2021). Finite Element Analysis of Composite Repair for Damaged Steel Pipeline. Coatings, 11(3). doi:10.3390/coatings11030301
  5. Le Thanh Cuong, Le Minh Hoang, Khatir, S., Wahab, M. A., Tran Minh Thi, & Mirjalili, S. (2021). A novel version of Cuckoo search algorithm for solving optimization problems. Expert Systems with Applications, 186, 115669. doi:https://doi.org/10.1016/j.eswa.2021.115669
  6. Dr. Benaissa, B., Kobayashi, M., Al Ali, M., Khatir, T., & Elaissaoui Elmeliani, M. E. A. (2024). Metaheuristic Optimization Algorithms: an overview. HCMCOU Journal of Science – Advances in Computational Structures, 14(1), 34-62. doi:10.46223/HCMCOUJS.acs.en.14.1.47.2024
  7. Gad, A. G. (2022). Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review. Archives of Computational Methods in Engineering, 29(5), 2531-2561. doi:10.1007/s11831-021-09694-4
  8. Gomes, G. F., Mendez, Y. A. D., da Silva Lopes Alexandrino, P., da Cunha, S. S., & Ancelotti, A. C. (2019). A Review of Vibration Based Inverse Methods for Damage Detection and Identification in Mechanical Structures Using Optimization Algorithms and ANN. Archives of Computational Methods in Engineering, 26(4), 883-897. doi:10.1007/s11831-018-9273-4
  9. Guerrero-Luis, M., Valdez, F., & Castillo, O. (2021). A Review on the Cuckoo Search Algorithm. In O. Castillo & P. Melin (Eds.), Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications (pp. 113-124). Cham: Springer International Publishing.
  10. Kaveh, A., & Eslamlou, A. D. (2020). Metaheuristic optimization algorithms in civil engineering: new applications, Switzerland: Springer International Publishing.
  11. Kaya, E., Gorkemli, B., Akay, B., & Karaboga, D. (2022). A review on the studies employing artificial bee colony algorithm to solve combinatorial optimization problems. Engineering Applications of Artificial Intelligence, 115, 105311. doi:https://doi.org/10.1016/j.engappai.2022.105311
  12. Khatir, A., Capozucca, R., Khatir, S., & Magagnini, E. (2022). Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial neural network. Frontiers of Structural and Civil Engineering, 16(8), 976-989.
  13. Khatir, A., Capozucca, R., Khatir, S., Magagnini, E., Benaissa, B., & Le Thanh Cuong (2024). An efficient improved Gradient Boosting for strain prediction in Near-Surface Mounted fiber-reinforced polymer strengthened reinforced concrete beam. Frontiers of Structural and Civil Engineering. doi:10.1007/s11709-024-1079-x
  14. Khatir, A., Capozucca, R., Khatir, S., Magagnini, E., Benaissa, B., Le Thanh Cuong, & Wahab, M. A. (2023). A new hybrid PSO-YUKI for double cracks identification in CFRP cantilever beam. Composite Structures, 311, 116803.
  15. Khatir, S., Tiachacht, S., Le Thanh Cuong, Ghandourah, E., Mirjalili, S., & Abdel Wahab, M. (2021). An improved Artificial Neural Network using Arithmetic Optimization Algorithm for damage assessment in FGM composite plates. Composite Structures, 273, 114287. doi:https://doi.org/10.1016/j.compstruct.2021.114287
  16. Kucukkoc, I., Aydin Keskin, G., Karaoglan, A. D., & Karadag, S. (2024). A hybrid discrete differential evolution – genetic algorithm approach with a new batch formation mechanism for parallel batch scheduling considering batch delivery. International Journal of Production Research, 62(1-2), 460-482. doi:10.1080/00207543.2023.2233626
  17. Li, J., Wei, X., Li, B., & Zeng, Z. (2022). A survey on firefly algorithms. Neurocomputing, 500, 662-678. doi:https://doi.org/10.1016/j.neucom.2022.05.100
  18. Makhadmeh, S. N., Al-Betar, M. A., Doush, I. A., Awadallah, M. A., Kassaymeh, S., Mirjalili, S., & Zitar, R. A. (2024). Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review. Ieee Access, 12, 22991-23028. doi:10.1109/ACCESS.2023.3304889
  19. Meraihi, Y., Gabis, A. B., Mirjalili, S., & Ramdane-Cherif, A. (2021). Grasshopper optimization algorithm: theory, variants, and applications. Ieee Access, 9, 50001-50024.
  20. Mitchell, E. B., Lucon, E., Collins, L. E., Clarke, A. J., & Clarke, K. D. (2021). Microstructure and Thickness Effects on Impact Behavior and Separation Formation in X70 Pipeline Steel. JOM, 73(6), 1966-1977. doi:10.1007/s11837-021-04562-9
  21. Na, J., Zhang, H., Lian, J., & Zhang, B. (2022). Partitioning DNNs for Optimizing Distributed Inference Performance on Cooperative Edge Devices: A Genetic Algorithm Approach. Applied Sciences, 12(20). Retrieved from doi:10.3390/app122010619
  22. Nadimi-Shahraki, M. H., Zamani, H., Asghari Varzaneh, Z., & Mirjalili, S. (2023). A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations. Archives of Computational Methods in Engineering, 30(7), 4113-4159. doi:10.1007/s11831-023-09928-7
  23. Nayar, N., Gautam, S., Singh, P., & Mehta, G. (2021, 2021//). Ant Colony Optimization: A Review of Literature and Application in Feature Selection. Paper presented at the Inventive Computation and Information Technologies, Singapore.
  24. Neves, A. C., González, I., Leander, J., & Karoumi, R. (2017). Structural health monitoring of bridges: a model-free ANN-based approach to damage detection. Journal of Civil Structural Health Monitoring, 7(5), 689-702. doi:10.1007/s13349-017-0252-5
  25. Oulad Brahim, A., Capozucca, R., Khatir, S., Fahem, N., Benaissa, B., & Le Thanh Cuong (2024). Optimal Prediction for Patch Design Using YUKI-RANDOM-FOREST in a Cracked Pipeline Repaired with CFRP. Arabian Journal for Science and Engineering. 1-18. doi:10.1007/s13369-024-08777-1
  26. Tran Ngoc Hoa, Khatir, S., De Roeck, G., Bui Tien Thanh, & Abdel Wahab, M. (2019). An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm. Engineering Structures, 199, 109637. doi:https://doi.org/10.1016/j.engstruct.2019.109637
  27. Wang, D., Tan, D., & Liu, L. (2018). Particle swarm optimization algorithm: an overview. Soft Computing, 22(2), 387-408. doi:10.1007/s00500-016-2474-6
  28. Zara, A., Belaidi, I., Fahem, N., Aribi, C., Oulad Brahim, A., Magagnini, E., & Khatir, A. (2024). Numerical study of the influence of layers number and their orientation in a CFRP using improved ANN based on Enhanced Jaya. HCMCOU Journal of Science – Advances in Computational Structures, 14(1). 20-33. doi:10.46223/HCMCOUJS.acs.en.14.1.46.2024
  29. Zenzen, R., Belaidi, I., Khatir, S., & Wahab, M. A. (2018). A damage identification technique for beam-like and truss structures based on FRF and Bat Algorithm. Comptes Rendus Mécanique, 346(12), 1253-1266.

Most read articles by the same author(s)