PSI - Issue 64

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Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2023) 000 – 000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2023) 000 – 000

www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia

ScienceDirect

Procedia Structural Integrity 64 (2024) 1524–1531

SMAR 2024 – 7th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures Machine Learning for Analyzing Concrete Cover Separation in Externally Bonded FRP RC Beams Manish Prasad a, *, Cristina Barris a , Mehdi Aghabagloo a , Beatriz López b , Ricardo Perera c , Marta Baena a a Analysis and Advanced Materials for Structural Design, AMADE, University of Girona, Girona 17003, Spain Abstract Strengthening of concrete structures with Externally Bonded (EB) Fiber Reinforced Polymer (FRP) is one the most cost effective, efficient, and sustainable rehabilitation techniques. However, usually the ultimate tensile strength of the FRP cannot be achieved due to the premature debonding of the FRP or delamination of the concrete cover. In this last case, known as concrete cover separation (CCS), high stress concentration at the free end of FRP laminate, in combination with the shear stress at that section, cause the initiation of a shear crack that, if not controlled by the shear reinforcement, propagates in concrete just below internal reinforcement. Various analytical and empirical models have been developed to predict this phenomenon but none of them could give a failure load with reasonable accuracy. On the other hand, Machine Learning (ML) has been proved very effective in predicting behaviors that are difficult to quantify using mechanics. This paper explores ML algorithms to predict the failure load of flexural elements that undergo CCS. To this end, a database has been collected that comprises 140 four-point bending tests on beams reinforced with EB CFRP that failed by CCS. K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBOOST) algorithms have been applied and then ranked based on their R-squared score. R-squared scores obtained from ML are the highest compared to the analytical models described in this paper. Moreover, the mean values and standard deviation of the ratio of experimental failure load and that of calculated by the ML model are the lowest compared to the analytical models. The predictions from the ML models are found more aligned with the experimental results than the analytical models. KNN is chosen as the best ML algorithm for the prediction of failure load with an R-squared score of 0.97. © 2024 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 responsibility of SMAR 2024 Organizers SMAR 2024 – 7th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures Machine Learning for Analyzing Concrete Cover Separation in Externally Bonded FRP RC Beams Manish Prasad a, *, Cristina Barris a , Mehdi Aghabagloo a , Beatriz López b , Ricardo Perera c , Marta Baena a a Analysis and Advanced Materials for Structural Design, AMADE, University of Girona, Girona 17003, Spain b Control Engineering and Intelligent Systems, EXIT, University of Girona, Girona 17003, Spain c Department of Mechanical Engineering, Technical University of Madrid, 28006, Madrid, Spain Abstract Strengthening of concrete structures with Externally Bonded (EB) Fiber Reinforced Polymer (FRP) is one the most cost effective, efficient, and sustainable rehabilitation techniques. However, usually the ultimate tensile strength of the FRP cannot be achieved due to the premature debonding of the FRP or delamination of the concrete cover. In this last case, known as concrete cover separation (CCS), high stress concentration at the free end of FRP laminate, in combination with the shear stress at that section, cause the initiation of a shear crack that, if not controlled by the shear reinforcement, propagates in concrete just below internal reinforcement. Various analytical and empirical models have been developed to predict this phenomenon but none of them could give a failure load with reasonable accuracy. On the other hand, Machine Learning (ML) has been proved very effective in predicting behaviors that are difficult to quantify using mechanics. This paper explores ML algorithms to predict the failure load of flexural elements that undergo CCS. To this end, a database has been collected that comprises 140 four-point bending tests on beams reinforced with EB CFRP that failed by CCS. K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBOOST) algorithms have been applied and then ranked based on their R-squared score. R-squared scores obtained from ML are the highest compared to the analytical models described in this paper. Moreover, the mean values and standard deviation of the ratio of experimental failure load and that of calculated by the ML model are the lowest compared to the analytical models. The predictions from the ML models are found more aligned with the experimental results than the analytical models. KNN is chosen as the best ML algorithm for the prediction of failure load with an R-squared score of 0.97. © 2024 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 responsibility of SMAR 2024 Organizers © 2024 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 responsibility of SMAR 2024 Organizers b Control Engineering and Intelligent Systems, EXIT, University of Girona, Girona 17003, Spain c Department of Mechanical Engineering, Technical University of Madrid, 28006, Madrid, Spain

Corresponding author: E-mail address: manish.prasad@udg.edu Corresponding author: E-mail address: manish.prasad@udg.edu

2452-3216 © 2024 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 responsibility of SMAR 2024 Organizers 2452-3216 © 2024 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 responsibility of SMAR 2024 Organizers

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