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All issues Volume 354 (2026) EPJ Web Conf., 354 (2026) 04008 References
Open Access
Issue
EPJ Web Conf.
Volume 354, 2026
19th Global Congress on Manufacturing and Management (GCMM 2025)
Article Number 04008
Number of page(s) 16
Section Digital Twins, IoT, and Smart Manufacturing Systems
DOI https://doi.org/10.1051/epjconf/202635404008
Published online 02 March 2026
  1. Ahmad, N., Rizwan, M., Ilyas, B., Hussain, S., Khan, M. U., Shakeel, H., & Ahmad, M. E, Nonlinear Modeling of RC Substandard Beam-Column Joints for Building Response Analysis in Support of Seismic Risk Assessment and Loss Estimation. Buildings, 12(10), 1758 (2022) [Google Scholar]
  2. Almustafa, M. K., & Nehdi, M. L, Novel hybrid machine learning approach for predicting structural response of RC beams under blast loading. In Structures (Vol. 39, pp. 1092–1106). Elsevier (2022). [Google Scholar]
  3. Zhang, T., Hou, Z., Chen, Q., Li, X., Fang, Y., Zhang, S., & Sun, W, A novel damage model integrated into the elastoplastic constitutive model and numerical simulations of reinforced concrete structures under cyclic loading. Journal of Building Engineering, 84, 108670 (2024) [Google Scholar]
  4. Liu, Y., Sun, B., Guo, T., & Li, Z, Multiscale damage analysis of engineering structures from material level to structural level: a systematic review. International Journal of Structural Integrity, 16(2), 275–310 (2025) [Google Scholar]
  5. Ahmed, B., Mangalathu, S., & Jeon, J. S, Seismic damage state predictions of reinforced concrete structures using stacked long short-term memory neural networks. Journal of Building Engineering, 46, 103737 (2022) [Google Scholar]
  6. Peng, J. Z., Pan, L. J., Wang, Z. Q., Gao, G. F., Wang, M., Hu, J., … & Wu, W. T, Self-adaptive graph neural network for predicting blast-induced damage in RC columns across multiple scenarios. Engineering Structures, 337, 120505 (2025) [Google Scholar]
  7. Tran, P. L., Tran, V. L., & Kim, J. K, Mid-span displacement and damage degree predictions of RC beams under blast loading using machine learning-based models. In Structures (Vol. 65, p. 106702). Elsevier (2024) [Google Scholar]
  8. Luo, H., & Paal, S. G, Data-driven seismic response prediction of structural components. Earthquake Spectra, 38(2), 1382–1416 (2022) [Google Scholar]
  9. Anas, S. M., Alam, M., & Saidani, M, Prediction of impact response of square reinforced concrete (RC) slab with square/circular opening under drop-weight impact using FEM simulation. Asian Journal of Civil Engineering, 25(2), 2189–2208 (2024) [CrossRef] [Google Scholar]
  10. Song, X., Cheng, X., Li, Y., Guo, R., Zhang, H., Liang, Z., & Wang, S, A numerical model database for rapid seismic damage assessment of typical regular reinforced concrete frame structures in urban building clusters. Journal of Building Engineering, 90, 109392 (2024) [Google Scholar]
  11. Zhang, C., Wen, W., Zhai, C., Jia, J., & Zhou, B, Structural nonlinear seismic time-history response prediction of urban-scale reinforced concrete frames based on deep learning. Engineering Structures, 317, 118702 (2024) [Google Scholar]
  12. Wen, W., Zhang, C., & Zhai, C, Rapid seismic response prediction of RC frames based on deep learning and limited building information. Engineering Structures, 267, 114638, (2022) [Google Scholar]
  13. Vrijdaghs, R., & Verstrynge, E, Probabilistic structural analysis of a real-life corroding concrete bridge girder incorporating stochastic material and damage variables in a finite element approach. Engineering Structures, 254, 113831 (2022) [Google Scholar]
  14. He, J., Gao, R., & Chen, J, A sparse data-driven stochastic damage model for seismic reliability assessment of reinforced concrete structures. Reliability Engineering & System Safety, 223, 108510 (2022) [Google Scholar]
  15. Xu, J. G., Feng, D. C., Mangalathu, S., & Jeon, J. S, Data-driven rapid damage evaluation for life-cycle seismic assessment of regional reinforced concrete bridges. Earthquake Engineering & Structural Dynamics, 51(11), 2730–2751 (2022) [Google Scholar]
  16. Verma, I., Gautam, L., Gomkale, S., Goyal, A., Berwal, P., Al Asmari, A. F., & Islam, S, Structural response of beam-column joint made of fiber reinforced concrete (FRC) using ANSYS FEA simulation. Rocz. Ochr. Srodowiska, 27, 174–195 (2025) [Google Scholar]
  17. Nasrul, K. B. M., Radzi, N. A. M., & Hamid, R, Simulation-based evaluation of reinforcement corrosion impact on the flexural performance of RC beams. Journal of Building Pathology and Rehabilitation, 10(1), 41 (2025) [Google Scholar]
  18. Osman, B. H, Experimental study on the behavior of pre-loaded reinforced concrete (RC) deep beams with openings strengthened with FRP sheets. World Journal of Engineering, 22(1), 148–159 (2025) [Google Scholar]
  19. Shelgaonkar, T., & Khante, S, Crack simulation and monitoring of beam-column joint by EMI technique using ANSYS. In Recent Trends in Construction Technology and Management: Select Proceedings of ACTM 2021 (pp. 1101–1113). Singapore: Springer Nature Singapore (2022) [Google Scholar]
  20. Nwankwo, C. O., & Mahachi, J, Analytical and numerical approaches in predicting the flexural behaviour of reinforced concrete beams. In The International Conference on Net-Zero Civil Infrastructures: Innovations in Materials, Structures, and Management Practices (NTZR) (pp. 1423–1435). Cham: Springer Nature Switzerland (2024) [Google Scholar]
  21. Zhang, C., Zhao, Y., Wu, G., Wu, H., Ding, H., Yu, J., & Wan, R, A correlation analysis-based structural load estimation method for RC beams using machine vision and numerical simulation. Buildings, 15(2), 207 (2025) [Google Scholar]
  22. Waqas, R. M., Elahi, A., & Kirgiz, M. S, Experimental and finite element analysis of shear deficient of reinforced concrete beam retrofitted externally with carbon fiber reinforced polymer sheet. In Structures (Vol. 72, p. 108232). Elsevier (2025) [Google Scholar]

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