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All issues Volume 664 (2025) E3S Web Conf., 664 (2025) 01014 References
Open Access
Issue
E3S Web Conf.
Volume 664, 2025
4th International Seminar of Science and Applied Technology: “Green Technology and AI-Driven Innovations in Sustainability Development and Environmental Conservation” (ISSAT 2025)
Article Number 01014
Number of page(s) 11
Section Artificial Intelligence and Human-Computer Interaction
DOI https://doi.org/10.1051/e3sconf/202566401014
Published online 20 November 2025
  1. World Health Organization. Global status report on road safety 2023. World Health Organization, Geneva (2023). https://www.who.int/teams/social-determinants-of- health/safety-and-mobility/global-status-report-on-road-safety-2023 9789240086517- eng [Google Scholar]
  2. C. A. Rosalia, G. R. Abramsyah, A. Jusuf, L. Gunawan, I. S. Putra, T. Dirgantara. Experimental & numerical study on hybrid aluminium/GFRP thin-walled column under quasi-static axial loading, Int. J. Crashworthiness (2024). https://doi.org/10.1080/13588265.2024.2321664 [Google Scholar]
  3. H. Mou, J. Xie, Z. Feng. Research status and future development of crashworthiness of civil aircraft fuselage structures: An overview, Prog. Aerosp. Sci. 119, 100644 (2020). https://doi.org/10.1016/j.paerosci.2020.100644 [Google Scholar]
  4. M. H. Mohd Hanid, S. Sharif, M. Ahmad, M. A. Suhaimi, C. Y. Khor, K. A. Ismail. Crashworthiness performance of thin-walled structures towards design configuration in vehicle crash boxes application: a review, Int. J. Crashworthiness, 1–31 (2025). https://doi.org/10.1080/13588265.2025.2492975 [Google Scholar]
  5. G. K. Niachanitya. Impact characteristics of crash box in vehicle – 01 (Nonlinear explicit buckling analysis of tube) [CAD model and tutorial], GrabCAD (2019). https://grabcad.com/library/impact-characteristics-of-crash-box-in-vehicle-01- nonlinear-explicit-buckling-analysis-of-tube-1 [Google Scholar]
  6. M. Capretti, G. Del Bianco, V. Giammaria, S. Boria. Natural Fibre and Hybrid Composite Thin-Walled Structures for Automotive Crashworthiness: A Review, Materials 17(10), 2246 (2024). https://doi.org/10.3390/ma17102246 [Google Scholar]
  7. L. Aydin, H. S. Artem. Design and optimization of fiber composites, in Fiber technology for fiber-reinforced composites, pp. 299–315, Woodhead Publishing (2017). https://doi.org/10.1016/B978-0-08-101871-2.00014-X [Google Scholar]
  8. M. F. M. Alkbir, A. M. S. Hamouda, S. M. Sapuan, E. Mahdi. Effect of geometry on crashworthiness parameters of natural kenaf fibre reinforced composite hexagonal tubes, Mater. Des. 58, 302–309 (2014) [Google Scholar]
  9. G. Sun, H. Jiang, J. Fang, G. Li, Q. Li. Parameterization of criss-cross configurations for multiobjective crashworthiness optimization, Int. J. Mech. Sci. 124, 145–157 (2017) [Google Scholar]
  10. J. Fang, G. Sun, N. Qiu, N. H. Kim, Q. Li. On design optimization for structural crashworthiness and its state of the art, Struct. Multidisc. Optim. 55, 1091–1119 (2017). https://doi.org/10.1007/s00158-016-1579-y [Google Scholar]
  11. X. Du, J. Liang, J. Lei, J. Xu, P. Xie. A radial-basis function mesh morphing and Bayesian optimization framework for vehicle crashworthiness design, Struct. Multidisc. Optim. 66, 64 (2023). https://doi.org/10.1007/s00158-023-03496-x [Google Scholar]
  12. A. Dadrasi, M. Shariati, G. A. Farzi, S. Fooladpanjeh. Experimental, Modeling, and Optimization Investigation on Mechanical Properties and the Crashworthiness of Thin- Walled Frusta of Silica/Epoxy Nano-composites: Fuzzy Neural Network, Particle Swarm Optimization/Multivariate Nonlinear Regression, and Gene Expression Programming, J. Mater. Eng. Perform. 31, 3030–3040 (2022). https://doi.org/10.1007/s11665-021-06391-y [Google Scholar]
  13. J. Verbraeken, M. Wolting, J. Katzy, J. Kloppenburg, T. Verbelen, J. S. Rellermeyer. A survey on distributed machine learning, ACM Comput. Surv. 53(2), 1–33 (2020). https://doi.org/10.1145/3377454 [Google Scholar]
  14. L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaría, M. A. Fadhel, M. Al-Amidie, L. Farhan. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, J. Big Data 8, 53 (2021). https://doi.org/10.1186/s40537-021-00444-8 [CrossRef] [Google Scholar]
  15. H. Mou, J. Zhang, Z. Feng. A triple-ANN prediction framework for axial compression performance of CFRP thin-walled C-columns with variable cross-section, Composite Structures 372, 119580 (2025). https://doi.org/10.1016/j.compstruct.2025.119580 [Google Scholar]
  16. R. J. Erb. Introduction to Backpropagation Neural Network Computation, Pharm. Res. 10, 165–170 (1993). https://doi.org/10.1023/A:1018966222807 [Google Scholar]
  17. M. Fernández-Delgado, E. Cernadas, S. Barro, D. Amorim. Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15, 3133–3181 (2014) [Google Scholar]
  18. S. Ham, S. Ji, S. S. Cheon. The Design of a Piecewise-Integrated Composite Bumper Beam with Machine-Learning Algorithms, Materials 17(3), 602 (2024). https://doi.org/10.3390/ma17030602 [Google Scholar]
  19. C. Schaffer. Overfitting avoidance as bias, Mach. Learn. 10, 153–178 (1993). https://doi.org/10.1007/BF00993504 [Google Scholar]
  20. X. Wu, T. Hu, N. Khodadadi, A. Nanni. Prediction on quasi-static compression deformation modes of circular tubes based on machine learning, Int. J. Mech. Sci. 287, 109993 (2025). https://doi.org/10.1016/j.ijmecsci.2025.109993 [Google Scholar]
  21. M.-K. Kazi, E. Mahdi. Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network, Compos. Part C: Open Access 13, 100440 (2024). https://doi.org/10.1016/j.jcomc.2024.100440 [Google Scholar]
  22. S. A. Shaikh, M. F. M. Taufique, K. Balusu, S. S. Kulkarni, F. Hale, J. Oleson, R. Devanathan, A. Soulaïmi. Finite element analysis and machine learning guided design of carbon fiber organosheet-based battery enclosures for crashworthiness. Appl. Compos. Mater. 31, 1475–1493 (2024). https://doi.org/10.1007/s10443-024-10218-z [Google Scholar]
  23. R. Liang, X. Tang, J. Huang, C. Bastien, C. Zhang, W. Tuo. A machine learning-based crashworthiness optimization for a novel pine cone-inspired multi-cell tubes under bending, Heliyon 10, e37828 (2024). https://doi.org/10.1016/j.heliyon.2024.e37828 [Google Scholar]
  24. T. T. Wong. Performance evaluation of classification algorithms by k-fold and leave- one-out cross validation, Pattern Recognit. 48(9), 2839–2846 (2015). https://doi.org/10.1016/j.patcog.2015.03.009 [Google Scholar]
  25. E. Sakardis, N. Karathanasopoulos, D. Mohr. Machine-learning based prediction of crash response of tubular structures, Int. J. Impact Eng. 166, 104318 (2022) [Google Scholar]
  26. X. Liu, R. Liang, Y. Hu. Optimization for Bending Collapse of Thin-Walled Twelve Right-Angle Section Tubes with Variable Thickness, Arab J. Sci. Eng. 49, 11523–11540 (2024). https://doi.org/10.1007/s13369-023-08658-z [Google Scholar]
  27. M.-K. Kazi, F. Eljack, E. Mahdi. Design of composite rectangular tubes for optimum crashworthiness performance via experimental and ANN techniques, Composite Structures 279, 114858 (2022). https://doi.org/10.1016/j.compstruct.2021.114858 [Google Scholar]
  28. D. Chen, Y. Li, K. Liu, Y. Li. A physics-informed neural network approach to fatigue life prediction using small quantity of samples, Int. J. Fatigue 166, 107270 (2023). https://doi.org/10.1016/j.ijfatigue.2022.107270 [Google Scholar]
  29. G. Colella, V. A. Lange, F. Duddeck. Transfer learning for crash design, Discov. Mech. Eng. 3, 26 (2024). https://doi.org/10.1007/s44245-024-00059-2 [Google Scholar]
  30. G. E. Karniadakis, I. G. Kevrekidis, L. Lu, P. Perdikaris, S. Wang, L. Yang. Physics- informed machine learning, Nat. Rev. Phys. 3(5), 422–440 (2021). [Google Scholar]
  31. S. A. Faroughi, N. M. Pawar, C. Fernandes, M. Raissi, S. Das, N. K. Kalantari, S. K. Mahjour. Physics-guided, physics-informed, and physics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics, J. Comput. Inf. Sci. Eng. 24(4), 040802 (2024). https://doi.org/10.1115/1.4064449 [Google Scholar]
  32. J. Liu, L. Xu, M. Yan, H. Zhang, H. Deng. Digital twin-based AAHP/CFRP battery enclosure hybrid optimization lightweight design method, ASME J. Risk Uncertain. Eng. B: Mech. Eng. Advance online publication (2025). https://doi.org/10.1115/1.4069678 [Google Scholar]
  33. W. Polini, A. Corrado. Digital twin of composite assembly manufacturing process, Int. J. Prod. Res. 58(17), 5238–5252 (2020). https://doi.org/10.1080/00207543.2020.1714091 [Google Scholar]

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