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Abstract

Beam-Column joints (BCJs) are critical components in reinforced concrete (RC) buildings. These members experience excessive stress during seismic events, often resulting in catastrophic failures, particularly in RC buildings constructed prior to the introduction of seismic design provisions and lacking reinforcement in the BCJ zone. The study presents a machine learning framework to predict the shear strength of unreinforced BCJs using 7 input parameters. This study developed an Artificial Neural Network-Finite Element Analysis hybrid model (ANN-FEA-13), trained, validated, and tested on 4320 samples of BCJ failure generated through nonlinear analysis in ABAQUS. The data was divided into training (70%), testing (15%), and validation (15%) sets. The ANN-FEA-13 model achieved high prediction accuracy (R = 0.962) and was compared with experimental data from literature and the ACI 318 code, showing superior performance. The results were promising and demonstrated the effectiveness of the developed data-driven ANN-FEA-13 framework, which reliably predicts BCJ failure and supports ongoing efforts in resilience-based assessment and retrofitting of aging RC structures in seismic regions.

Keywords

Artificial neural network (ANN) Finite element analysis (FEA) Reinforced concrete Beam-column joint Shear strength prediction Seismic resilience

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Maatouq, R., & Hdeib, R. (2026). A framework for integrating artificial neural networks and finite element analysis for shear strength prediction in unreinforced RC beam–column joints . Future Technology, 5(4), 1–14. Retrieved from https://fupubco.com/futech/article/view/1023
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References

  1. S. O. Shaaban IG, “Experimental behavior of full-scale exterior beam-column space joints retrofitted by ferrocement layers under cyclic loading,” Case Studies in Construction, vol. 8, p. 61–78, 2018.
  2. J. H. Haido, “Prediction of the shear strength of RC beam-column joints using new ANN formulations,” Structures, vol. 38, p. 1191–1209, 2022.
  3. S. M. Allam, H. M. Elbakry and I. S. Arab, “Exterior reinforced concrete beam column joint subjected to monotonic loading,” Alexandria Engineering Journal, vol. 57, p. 4133–4144, 2018.
  4. L. S. R. Salinas Guayacundo DR, “Nonlinear analysis of unreinforced beam-column joints,” Inge CuC, vol. 16, p. 129–140, 2020.
  5. B. M. E.-K. N. Salado Castillo JG, “Seismic resilience of building inventory towards resilient cities,” Resilient Cities and Structures, vol. 1, p. 1–12, 2022.
  6. B. M. S. A. S. K. Singh RR, “Resilience deficit index for quantification of resilience,” Resilient Cities and Structures, vol. 1, pp. 1-9, 2022.
  7. Z. C. Christopoulos C, “Towards understanding, estimating and mitigating higher mode effects for more resilient tall buildings,” Resilient Cities and Structures, vol. 1, p. 53–64, 2022.
  8. M. H. El-Naqeeb, B. S. Abdelwahed and S. E. El-Metwally, “Numerical investigation of RC exterior beam-column connection with different joint reinforcement detailing,” Institution of Structural Engineers, vol. 38, p. 1570–1581, 2022.
  9. E. Ercan, B. Arisoy and a. O. B. Ertem, “Experimental Assessment of RC Beam-Column Connections with Internal and External Strengthening Techniques,” Advances in Civil Engineering, vol. 2019, 2019.
  10. J. Melo, H. Varum and T. Rossetto, “Experimental assessment of the monotonic and cyclic behaviour of exterior RC beam-column joints built with plain bars and non-seismically designed,” Engineering Structures, vol. 270, p. 114887, 2022.
  11. A. A. Maseer MS, “Enhancing performance of beam-column joints in reinforced concrete structures using carbon fiber-reinforced polymers (CFRP): A novel review,” Hybrid Advances 10 , 2025.
  12. O. Algassem and R. L. Vollum, “Behaviour and design of monotonically loaded reinforced concrete external beam-column joints,” Structures, vol. 52, p. 946–970, 2023.
  13. M. Risi, P. Ricci, G. Verderame and G. Manfredi, “Experimental assessment of unreinforced exterior beam-column joints,” in 16th World Conference on Earthquake, 16WCEE 2017, Santiago Chile, 2017.
  14. C. R. Y. B. M. R. Mucedero G, “Estimation of seismic downtime for building retrofitting decision-making. Resilient Cities and Structures,” vol. 4, p. 15–29, 2025.
  15. S. IH, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN Comput Sci 2, 2021.
  16. S. Golnaraghi, Z. Zangenehmadar, O. Moselhi and S. Alkass, “Application of Artificial Neural Network(s) in Predicting Formwork Labour Productivity,” Advances in Civil Engineering, vol. 2019, 2019.
  17. S. Mangalathu, G. Heo and J.-S. Jeon, “Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes,” Engineering Structures, vol. 162, pp. 166-176, 2018.
  18. S. Chatterjee, S. Sarkar, S. Hore, D. Nilanjan, A. Ashour, F. Shi and D. Le, “Structural failure classification for reinforced concrete buildings using trained neural network based multi objective genetic algorithm,” Structural Engineering and Mechanics, vol. 63, no. 4, pp. 1598-6217, 2017.
  19. D.-C. Feng, Z.-T. Liu, X.-D. Wang, Z.-M. Jiang and S.-X. Liang, “Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm,” Advanced Engineering Informatics, vol. 45, 2020.
  20. B. Todorov and A. MuntasirBillah, “Machine learning driven seismic performance limit state identification for performance-based seismic design of bridge piers,” Engineering Structures, vol. 255, no. 0141-0296, 2022.
  21. H. Nguyen, T. Vu, T. P. Vo and H.-T. Thai, “Efficient machine learning models for prediction of concrete strengths,” Construction and Building Materials, vol. 266, no. 0950-0618, 2020.
  22. J. Xu, W. Hong, J. Zhang, S. Hou and G. Wu, “Seismic performance assessment of corroded RC columns based on data-driven machine-learning approach,” Engineering Structures, vol. 255, no. 0141-0296, 2022.
  23. L. Ali, F. Alnajjar, A. Jassmi, M. Gocho, W. Khan and A. Serhani, “Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures,” Sensors, vol. 21, no. 5, p. 1688, 2021.
  24. Z. L. A. R. H. R. Wang G, “Deformation and stress theory of surrounding rock of shallow circular tunnel based on complex variable function method,” Applied Mathematics and Nonlinear Sciences, vol. 7, p. 629–640, 2022.
  25. A. M. Wakjira TG, “Hybrid machine learning-enabled multivariate bridge-specific seismic vulnerability and resilience assessment of UHPC bridges,” Resilient Cities and Structures, vol. 4, p. 92–102, 2025.
  26. L. R. Hashemi A, “Seismic performance evaluation of mass timber buildings equipped with resilient and conventional friction devices,” Resilient Cities and Structures, vol. 4, p. 103–115, 2025.
  27. A. A. H. Alwanas, A. A. Al-Musawi, S. Q. Salih, H. Tao, M. Ali and Z. M. Yaseen, “Load-carrying capacity and mode failure simulation of beam-column joint connection: Application of self-tuning machine learning model,” Engineering Structures, vol. 194, p. 220–229, 2019.
  28. H. S. Marie, K. A. El-Hassan, E. M. Almetwally and M. A. El-Mandouh, “Joint shear strength prediction of beam-column connections using machine learning via experimental results,” Case Studies in Construction Materials, vol. 17, p. e01463, 2022.
  29. S. Ramavath and S. R. Suryawanshi, “Optimal Prediction of Shear Properties in Beam-Column Joints Using Machine Learning Approach,” International Journal of Engineering, vol. 37, no. 01, pp. 67-82, 2024.
  30. M. M. A. A. e. a. Jayasinghe SC, “ A review on the applications of artificial neural network techniques for accelerating finite element analysis in the civil engineering domain,” Comput Struct 310.
  31. B. R. Infante V, “Non-Proportional mixed mode plastic zones via finite elements and artificial neural networks,” Theoretical and Applied Fracture Mechanics, p. 135.
  32. L. N. S. A. T. E. Chen PY, “A Method for automated development of model and fragility inventories of nonductile reinforced concrete buildings,” Resilient Cities and Structures, vol. 2, pp. 87-103, 2023.
  33. ABAQUS, Abaqus Analysis User’s Guide 6.13, Providence, RI: Dassault Systèmes Simulia Corp, 2013. https://wikifab.org/wiki/Abaqus_documentation_6.13_pdf
  34. K. Creel, “Transparency in Complex Computational Systems,” Philosophy of Science, vol. 87, no. 4, pp. 568-589, 2020.
  35. M. Ajmal, D. Ahmed, M. H. Baluch, M. K. Rahman and T. Ayadat, “Consistent choice for cohesion and internal friction for concrete constitutive models,” Innovative Infrastructure Solutions, vol. 8, no. 43, 2023.
  36. ACI 318-19 Committee, Building Code Requirements for Structural Concrete (ACI 318-19), Farmington Hills, Michigan : American Concrete Institute, 2019.
  37. J. B. Mander, M. J. N. Priestley and R. Park, “THEORETICAL STRESS-STRAIN MODEL FOR CONFINED CONCRETE,” Journal of Structural Engineering, vol. 114, no. 8, pp. 1804-1826, 1988.
  38. S. J. Z. Y. Y. S. a. M. M. E. Ağcakoca, “Advanced Hybrid Modeling of Cementitious Composites Using Machine Learning and Finite Element Analysis Based on the CDP Model,” Buildings, 2025.
  39. L. C. A. Y. T. J. a. J. S. K. Zheng, “Study on a Layered Finite Element Method for Hollow Concrete‐Filled Steel Tube Columns,” Advances in Civil Engineering, 2025.
  40. S. A. M. I. K. M. A. G. S. A. a. A. K. M. Ali, “Experimental Validation of Mander’s Model for Low Strength Confined Concrete Under Axial Compression,” in Second International Sustainability and Resilience Conference: Technology and Innovation in Building Designs(51154), 2020.
  41. B. Massicotte, A. E. Elwi and J. G. MacGregor, “Tension stiffening model for planar reinforced concrete members,” ASCE Journal of Structural Engineering, vol. 116, no. 11, p. 3039–3058, 1990.
  42. M. Ajmal, Cyclic Response of Beam Column Joints Strengthened with Superelastic Shape Memory Alloys (SMAs) (PhD thesis), Dhahran: King Fahd University of Petroleum and Minerals, 2016.
  43. S. J. Hamil, Reinforced Concrete Beam-Column Connection Behaviour (PhD thesis), Durham, England: School of Engineering, University of Durham, 2000.
  44. The MathWorks Inc., MATLAB version: 9.7.0 (R2019b), Natick, Massachusetts: The MathWorks Inc., 2019.
  45. S. Alagundi and T. Palanisamy, “Neural network prediction of joint shear strength of exterior beam-column joint,” Structures, vol. 37, p. 1002–1018, 2022.
  46. S. IH, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN Comput Sci 2 , 2021. https://doi.org/10.1007/s42979-021-00592-x
  47. K. A. M. Ali, C. Li, W. Han, S. Issa, M. H. Eid, S. F. Mahmoud and M. A.-E. Mohammed., “Performance evaluation and prediction of optimal operational conditions for a compact date seeds milling unit using feedforward neural networks,” Scientific Reports, vol. 15, p. 4764 , 2025.
  48. M. Bauchy, Artificial Neural Networks and Deep Learning – Machine Learning for Engineers, California, Los Angeles: University of California, 2020. https://catalog.registrar.ucla.edu/course/2021/ecengrc147?siteYear=2021