PSI - Issue 70

ScienceDirect Structural Integrity Procedia 00 (2025) 000 – 000 Structural Integrity Procedia 00 (2025) 000 – 000 Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Available online at www.sciencedirect.com ScienceDirect

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Procedia Structural Integrity 70 (2025) 386–393

Structural Integrity and Interactions of Materials in Civil Engineering Structures (SIIMCES-2025) Interpretable Ensemble Machine Learning Models for Prediction of Shear Strength of Concrete Beams Reinforced with FRP Rebar Rachit Sharma a, *, Arghadeep Laskar a a Department of Civil Engineering, Indian Institute of Technology, Bombay, Powai, India – 400076 Abstract A lack of design guidelines in Indian Standard code for fiber reinforced polymer (FRP) bars could result in over-reinforcement, driving up construction costs. The current design guidelines for shear strength vary based on several parameters and often provide conservative results. To address this issue, a machine learning (ML) framework based on ensembled models has been developed to estimate the shear capacity of FRP reinforced concrete (FRP-RC) beams. A well-curated dataset consisting of RC beams with FRP rebars without stirrups has been utilized for this purpose. An ensemble algorithm, XGBoost, and a traditional algorithm, Support Vector Regressor (SVR), have been compared for evaluating the shear capacity. The algorithms' hyperparameters were optimized using GridSearch on the training set, combined with a ten-fold cross-validation optimization method. The models' performance has been assessed using four metrics: R² score, RMSE, MAE, and MAPE. The best performing XGBoost model has achieved performance metrics of 0.94, 34.39 kN, 14.68 kN and 15.80% on testing dataset for R 2 , RMSE, MAE and MAPE respectively. The model's reliability has been further confirmed by comparing it with the current design codes ACI 440-1.R15 and GB50608-2020. Since ML-based models function as black boxes, a unified SHapley Additive exPlanations (SHAP) approach has been applied to interpret the outcome of XGBoost model. The beam width ( b ), span-to-depth ( a/d ) and effective depth ( h ) have been identified as the most significant input features which can improve the shear strength predictability for FRP-RC beams. © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under the responsibility of International Conference on Structural Integrity Organizers Keywords: Shear strength; FRP rebar; Machine learning; Ensemble models; SHAP; Structural Integrity and Interactions of Materials in Civil Engineering Structures (SIIMCES-2025) Interpretable Ensemble Machine Learning Models for Prediction of Shear Strength of Concrete Beams Reinforced with FRP Rebar Rachit Sharma a, *, Arghadeep Laskar a a Department of Civil Engineering, Indian Institute of Technology, Bombay, Powai, India – 400076 Abstract A lack of design guidelines in Indian Standard code for fiber reinforced polymer (FRP) bars could result in over-reinforcement, driving up construction costs. The current design guidelines for shear strength vary based on several parameters and often provide conservative results. To address this issue, a machine learning (ML) framework based on ensembled models has been developed to estimate the shear capacity of FRP reinforced concrete (FRP-RC) beams. A well-curated dataset consisting of RC beams with FRP rebars without stirrups has been utilized for this purpose. An ensemble algorithm, XGBoost, and a traditional algorithm, Support Vector Regressor (SVR), have been compared for evaluating the shear capacity. The algorithms' hyperparameters were optimized using GridSearch on the training set, combined with a ten-fold cross-validation optimization method. The models' performance has been assessed using four metrics: R² score, RMSE, MAE, and MAPE. The best performing XGBoost model has achieved performance metrics of 0.94, 34.39 kN, 14.68 kN and 15.80% on testing dataset for R 2 , RMSE, MAE and MAPE respectively. The model's reliability has been further confirmed by comparing it with the current design codes ACI 440-1.R15 and GB50608-2020. Since ML-based models function as black boxes, a unified SHapley Additive exPlanations (SHAP) approach has been applied to interpret the outcome of XGBoost model. The beam width ( b ), span-to-depth ( a/d ) and effective depth ( h ) have been identified as the most significant input features which can improve the shear strength predictability for FRP-RC beams. © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under the responsibility of International Conference on Structural Integrity Organizers Keywords: Shear strength; FRP rebar; Machine learning; Ensemble models; SHAP; © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under the responsibility of International Conference on Structural Integrity Organizers

* Corresponding author. Mob.: +91-8219905872. E-mail address: rachit_sharma@iitb.ac.in; rachitrooney10@gmail.com * Corresponding author. Mob.: +91-8219905872. E-mail address: rachit_sharma@iitb.ac.in; rachitrooney10@gmail.com

2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under the responsibility of International Conference on Structural Integrity Organizers 2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under the responsibility of International Conference on Structural Integrity Organizers

2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under the responsibility of International Conference on Structural Integrity Organizers 10.1016/j.prostr.2025.07.068