Saad A. Yehia, Sabry Fayed, Mohamed H. Zakaria, Ramy I. Shahin
{"title":"基于机器学习的钢筋混凝土 T 梁抗剪强度预测","authors":"Saad A. Yehia, Sabry Fayed, Mohamed H. Zakaria, Ramy I. Shahin","doi":"10.1186/s40069-024-00690-z","DOIUrl":null,"url":null,"abstract":"<p>The contribution of shear resisted by flanges of T-beams is usually ignored in the shear design models even though it was proven by many experimental studies that the shear strength of T-beams is higher than that of equivalent rectangular cross-sections. Ignoring such a contribution result in a very conservative and uneconomical design. Therefore, the aim of this research is to investigate the capability of machine learning (ML) techniques to predict the shear capacity of reinforced concrete T-beams (RCTBs) by incorporating the contribution of the flange. Five machine learning (ML) techniques, which are the Decision Tree (DT), Random Forest (RF), Gradient Boosting Regression Tree (GBRT), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost), are trained and tested using 360 sets of data collected from experimental studies. Among the various machine learning models evaluated, the XGBoost model demonstrated exceptional reliability and precision, achieving an R-squared value of 99.10%. The SHapley Additive exPlanations (SHAP) approach is utilized to identify the most influential input features affecting the predicted shear capacity of RCTBs. The SHAP results indicate that the shear span-to-depth ratio (a/d) has the most significant effect on the shear capacity of RCTBs, followed by the ratio of shear reinforcement multiplied by the yield strength of shear reinforcement (<span>\\({\\rho }_{{\\text{v}}}{f}_{{\\text{yv}}}\\)</span>), flange thickness (<span>\\({h}_{{\\text{f}}}\\)</span>), and flange width (<span>\\({b}_{{\\text{f}}}\\)</span>). The accuracy of the XGBoost model in predicting the shear capacity of RCTBs is compared with established codes of practice (ACI 318-19, BS 8110-1:1997, EN 1992-1-2, CSA23.3-04) and existing formulas from researchers. This comparison reinforces the superior reliability and accuracy of the machine learning approach compared to traditional methods. Furthermore, a user-friendly interface platform is developed, effectively simplifying the implementation of the proposed machine-learning model. The reliability analysis is performed to determine the value of the resistance reduction factor (ϕ) that will achieve a target reliability index (<span>\\({\\beta }_{T}\\)</span>= 3.5).</p>","PeriodicalId":13832,"journal":{"name":"International Journal of Concrete Structures and Materials","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of RC T-Beams Shear Strength Based on Machine Learning\",\"authors\":\"Saad A. Yehia, Sabry Fayed, Mohamed H. Zakaria, Ramy I. 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Among the various machine learning models evaluated, the XGBoost model demonstrated exceptional reliability and precision, achieving an R-squared value of 99.10%. The SHapley Additive exPlanations (SHAP) approach is utilized to identify the most influential input features affecting the predicted shear capacity of RCTBs. The SHAP results indicate that the shear span-to-depth ratio (a/d) has the most significant effect on the shear capacity of RCTBs, followed by the ratio of shear reinforcement multiplied by the yield strength of shear reinforcement (<span>\\\\({\\\\rho }_{{\\\\text{v}}}{f}_{{\\\\text{yv}}}\\\\)</span>), flange thickness (<span>\\\\({h}_{{\\\\text{f}}}\\\\)</span>), and flange width (<span>\\\\({b}_{{\\\\text{f}}}\\\\)</span>). The accuracy of the XGBoost model in predicting the shear capacity of RCTBs is compared with established codes of practice (ACI 318-19, BS 8110-1:1997, EN 1992-1-2, CSA23.3-04) and existing formulas from researchers. This comparison reinforces the superior reliability and accuracy of the machine learning approach compared to traditional methods. Furthermore, a user-friendly interface platform is developed, effectively simplifying the implementation of the proposed machine-learning model. 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Prediction of RC T-Beams Shear Strength Based on Machine Learning
The contribution of shear resisted by flanges of T-beams is usually ignored in the shear design models even though it was proven by many experimental studies that the shear strength of T-beams is higher than that of equivalent rectangular cross-sections. Ignoring such a contribution result in a very conservative and uneconomical design. Therefore, the aim of this research is to investigate the capability of machine learning (ML) techniques to predict the shear capacity of reinforced concrete T-beams (RCTBs) by incorporating the contribution of the flange. Five machine learning (ML) techniques, which are the Decision Tree (DT), Random Forest (RF), Gradient Boosting Regression Tree (GBRT), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost), are trained and tested using 360 sets of data collected from experimental studies. Among the various machine learning models evaluated, the XGBoost model demonstrated exceptional reliability and precision, achieving an R-squared value of 99.10%. The SHapley Additive exPlanations (SHAP) approach is utilized to identify the most influential input features affecting the predicted shear capacity of RCTBs. The SHAP results indicate that the shear span-to-depth ratio (a/d) has the most significant effect on the shear capacity of RCTBs, followed by the ratio of shear reinforcement multiplied by the yield strength of shear reinforcement (\({\rho }_{{\text{v}}}{f}_{{\text{yv}}}\)), flange thickness (\({h}_{{\text{f}}}\)), and flange width (\({b}_{{\text{f}}}\)). The accuracy of the XGBoost model in predicting the shear capacity of RCTBs is compared with established codes of practice (ACI 318-19, BS 8110-1:1997, EN 1992-1-2, CSA23.3-04) and existing formulas from researchers. This comparison reinforces the superior reliability and accuracy of the machine learning approach compared to traditional methods. Furthermore, a user-friendly interface platform is developed, effectively simplifying the implementation of the proposed machine-learning model. The reliability analysis is performed to determine the value of the resistance reduction factor (ϕ) that will achieve a target reliability index (\({\beta }_{T}\)= 3.5).
期刊介绍:
The International Journal of Concrete Structures and Materials (IJCSM) provides a forum targeted for engineers and scientists around the globe to present and discuss various topics related to concrete, concrete structures and other applied materials incorporating cement cementitious binder, and polymer or fiber in conjunction with concrete. These forums give participants an opportunity to contribute their knowledge for the advancement of society. Topics include, but are not limited to, research results on
Properties and performance of concrete and concrete structures
Advanced and improved experimental techniques
Latest modelling methods
Possible improvement and enhancement of concrete properties
Structural and microstructural characterization
Concrete applications
Fiber reinforced concrete technology
Concrete waste management.