PSI - Issue 70

Rachit Sharma et al. / Procedia Structural Integrity 70 (2025) 386–393 Sharma and Laskar/ Structural Integrity Procedia 00 (2025) 000 – 000

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its reliability and rationality, while further exploring the impact of individual parameters of the shear strength prediction. The following conclusions are drawn from the present study: • The current design codes such as ACI440.R1-15 and GB50608-2020 are conservative indicating higher prediction ratio ( V exp / V pre ) for shear strength effectively increasing construction cost using FRP rebar. • The ML models showed significantly higher predictions capability with XGBoost model outperforming SVR model and analytical equations using codes in terms of performance matrices with least MAE, RMSE and MAPE and higher R 2 for the dataset. • The existing shear design method in the Indian Standard code relies solely on f c and . However, its development can be enhanced by incorporating additional influencing factors such as shear span to effective depth ratio (a/d) and FRP elastic modulus (E f ) , as demonstrated through SHAP values. The proposed model enables further predictions on new data based on the calibrated machine learning framework. Additionally, a graphical user interface (GUI) for utilizing the model for practical practice is currently in development and will be made available upon request via email correspondence. Appendix A The database used in the study is available as a downloadable cloud link at: https://23061993 my.sharepoint.com/:b:/g/personal/rachit_sharma_23061993_onmicrosoft_com/EZxpf9ZPI_1JuDW9IIRLYacBl2H1 oUbMalC0-TZS3ZFtcA?e=gKdQHDab References ACI 440.1 R-15, 2015. Guide for the design and construction of structural concrete reinforced with fiber reinforced polymer (FRP) bars. ACI Committee 318, 2019. Building Code Requirements for Structural Concrete (ACI 318-19) and Commentary (ACI 318R-19). American Concrete Institute, Farmington Hills, MI. Alam, M.S., Asce, M., 2023. Shear Strength Prediction of Slender Concrete Beams Reinforced with FRP Rebar Using Data-Driven Machine Learning Algorithms. 02, 1 – 18. https://doi.org/10.1061/(ASCE)CC.1943-5614.0001280 Alam, S., Sultana, N., Hossain, S.M.Z., 2021. Bayesian optimization algorithm based support vector regression analysis for estimation of shear capacity of FRP reinforced concrete members. Applied Soft Computing 105, 107281. https://doi.org/10.1016/j.asoc.2021.107281 Ali, A.H., Mohamed, H.M., Chalioris, C.E., Deifalla, A., 2021. Evaluating the shear design equations of FRP-reinforced concrete beams without shear reinforcement. Engineering Structures 235, 112017. https://doi.org/10.1016/j.engstruct.2021.112017 CSA (Canadian Standards Association), 2012. Design and construction of building components with fiber reinforced polymers. (CSA-S806-12). El Zareef, M.A., Elbisy, M.S., Badawi, M., 2021. Evaluation of code provisions predicting the concrete shear strength of FRP-reinforced members without shear reinforcement. Composite Structures 275, 114430. https://doi.org/10.1016/j.compstruct.2021.114430 European Standard, 2023. EN 1992-1-1 Eurocode 2: Design of Concrete Structures - Part 1- 1 : General Rules and Rules for Buildings., The European Union Standard. GB 50608 – 2020, 2020. Technical code for infrastructure application of FRP composites. Hutter, F., Kotthoff, L., Vanschoren, J., 2019. Automated machine learning: methods, systems, challenges. Springer Nature. Johnson, D.T., Sheikh, S.A., 2016. Experimental Investigation of Glass Fiber-Reinforced Polymer-Reinforced Normal-Strength Concrete Beams. ACI Structural Journal 113. https://doi.org/10.14359/51689017 JSCE (Japan Society of Civil Engineers),1997. Recommendation for design and construction of concrete structures using continuous fiber reinforcing materials. 1 – 58. Kuo, P.-C., Chou, Y.-T., Li, K.-Y., Chang, W.-T., Huang, Y.-N., Chen, C.-S., 2020. GNN-LSTM-based fusion model for structural dynamic responses prediction. Engineering Structures 306, 117733. https://doi.org/10.1016/j.engstruct.2024.117733 Li, Y., Chen, H., Yi, W.-J., Peng, F., Li, Z., Zhou, Y., 2021 Effect of member depth and concrete strength on shear strength of RC deep beams without transverse reinforcement. Engineering Structures 241, 112427. https://doi.org/10.1016/j.engstruct.2021.112427 Lundberg, S.M., Lee, S.-I., 2017 A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems 30. Machial, R., Alam, M.S., Rteil, A., 2016. Optimized shear design equation for slender concrete beams reinforced with FRP bars and stirrups using Genetic Algorithm and reliability analysis. Engineering Structures 107, 151 – 165. https://doi.org/10.1016/j.engstruct.2015.10.049 Nakamura, H., 1995. Evaluation of shear strength of continuous fiber reinforced concrete beams. Journal of the Japan Society of Civil Engineers. Nasrollahzadeh, K., Aghamohammadi, R., 2018. Reliability analysis of shear strength provisions for FRP-reinforced concrete beams. Engineering Structures 176, 785 – 800. https://doi.org/10.1016/j.engstruct.2018.09.016 Razaqpur, A.G., Isgor, B.O., Asce, A.M., Greenaway, S., Selley, A., 2004. Concrete Contribution to the Shear Resistance of Fiber Reinforced

Polymer Reinforced Concrete Members. 8, 452 – 460. https://doi.org/10.1061/(ASCE)1090-0268(2004)8 Tottori, S., Wakui, H., 1993. Shear Capacity of RC and PC Beams Using FRP Reinforcement. 615 – 632.