Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Machine Learning Driven Shear Strength Prediction Model for FRP-Reinforced Concrete Beams
The shear strength prediction of a fiber-reinforced polymer (FRP)-reinforced concrete (RC) beam is a difficult undertaking that is influenced by several design variables. While the usage of FRP bars has emerged as a viable alternative in reducing corrosion problems associated with steel reinforcement in an extreme environment, a precise and reliable method of shear strength prediction is required to assure cost-effective material use and optimum design. Several design provisions and optimized design equations are now available in the current literature; nevertheless, when these equations are used, a significant variation between the experimental and the predicted shear strength of FRP-RC beams is observed. This study utilised the power of data-driven modelling techniques for enhanced prediction capability of such a complex phenomenon. The objective of the current study is to develop a data-driven shear strength prediction model for FRP-RC slender beams using the extreme gradient boosting (XGBoost) algorithm. A large database of 302 tests of RC beams longitudinally reinforced with FRP bars without stirrups was collected from the available literature. The performance of the proposed ML model is compared with the existing standards, codes, guidelines, and optimized shear strength equation. The results reveal that the XGBoost model outperformed the existing shear strength provisions and has a high level of prediction accuracy.
Machine Learning Driven Shear Strength Prediction Model for FRP-Reinforced Concrete Beams
The shear strength prediction of a fiber-reinforced polymer (FRP)-reinforced concrete (RC) beam is a difficult undertaking that is influenced by several design variables. While the usage of FRP bars has emerged as a viable alternative in reducing corrosion problems associated with steel reinforcement in an extreme environment, a precise and reliable method of shear strength prediction is required to assure cost-effective material use and optimum design. Several design provisions and optimized design equations are now available in the current literature; nevertheless, when these equations are used, a significant variation between the experimental and the predicted shear strength of FRP-RC beams is observed. This study utilised the power of data-driven modelling techniques for enhanced prediction capability of such a complex phenomenon. The objective of the current study is to develop a data-driven shear strength prediction model for FRP-RC slender beams using the extreme gradient boosting (XGBoost) algorithm. A large database of 302 tests of RC beams longitudinally reinforced with FRP bars without stirrups was collected from the available literature. The performance of the proposed ML model is compared with the existing standards, codes, guidelines, and optimized shear strength equation. The results reveal that the XGBoost model outperformed the existing shear strength provisions and has a high level of prediction accuracy.
Machine Learning Driven Shear Strength Prediction Model for FRP-Reinforced Concrete Beams
Lecture Notes in Civil Engineering
Gupta, Rishi (Herausgeber:in) / Sun, Min (Herausgeber:in) / Brzev, Svetlana (Herausgeber:in) / Alam, M. Shahria (Herausgeber:in) / Ng, Kelvin Tsun Wai (Herausgeber:in) / Li, Jianbing (Herausgeber:in) / El Damatty, Ashraf (Herausgeber:in) / Lim, Clark (Herausgeber:in) / Karim, Mohammad Rezaul (Autor:in) / Islam, Kamrul (Autor:in)
Canadian Society of Civil Engineering Annual Conference ; 2022 ; Whistler, BC, BC, Canada
Proceedings of the Canadian Society of Civil Engineering Annual Conference 2022 ; Kapitel: 70 ; 1033-1044
06.08.2023
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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