Prediction of RC T-Beams Shear Strength Based on Machine Learning

IF 3.6 3区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Saad A. Yehia, Sabry Fayed, Mohamed H. Zakaria, Ramy I. Shahin
{"title":"Prediction of RC T-Beams Shear Strength Based on Machine Learning","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":"13 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Concrete Structures and Materials","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s40069-024-00690-z","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

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).

Abstract Image

基于机器学习的钢筋混凝土 T 梁抗剪强度预测
在剪力设计模型中,通常会忽略 T 型梁翼缘板抵抗剪力的贡献,尽管许多实验研究证明 T 型梁的剪力强度高于等效矩形截面。忽略这种作用会导致设计非常保守且不经济。因此,本研究旨在研究机器学习(ML)技术通过考虑翼缘的贡献来预测钢筋混凝土 T 型梁(RCTB)抗剪能力的能力。利用从实验研究中收集的 360 组数据对五种机器学习(ML)技术进行了训练和测试,这五种技术分别是决策树(DT)、随机森林(RF)、梯度提升回归树(GBRT)、轻梯度提升机(LightGBM)和极端梯度提升(XGBoost)。在接受评估的各种机器学习模型中,XGBoost 模型表现出卓越的可靠性和精确性,R 平方值达到 99.10%。SHapley Additive exPlanations(SHAP)方法用于识别影响 RCTB 剪切能力预测的最有影响力的输入特征。SHAP 结果表明,剪切跨度与深度比 (a/d) 对 RCTB 的剪切承载力影响最大、其次是剪力配筋比乘以剪力配筋屈服强度({\rho }_{\{v}}{f}_{{\{yv}}})、翼缘厚度(\({h}_{\{f}}}\)和翼缘宽度(\({b}_{\{f}}}\)。我们将 XGBoost 模型预测 RCTB 受剪承载力的准确性与已有的实践规范(ACI 318-19、BS 8110-1:1997、EN 1992-1-2、CSA23.3-04)和研究人员的现有公式进行了比较。与传统方法相比,机器学习方法的可靠性和准确性更胜一筹。此外,还开发了一个用户友好界面平台,有效简化了拟议机器学习模型的实施。可靠性分析的目的是确定能够实现目标可靠性指数(\({\beta }_{T}\)= 3.5)的电阻减小因子 (j)值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
International Journal of Concrete Structures and Materials
International Journal of Concrete Structures and Materials CONSTRUCTION & BUILDING TECHNOLOGY-ENGINEERING, CIVIL
CiteScore
6.30
自引率
5.90%
发文量
61
审稿时长
13 weeks
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信