{"title":"用于预测再生混凝土抗弯和抗拉强度的实验研究和机器学习模型:弥合可持续材料与结构设计之间的差距","authors":"Syed Minhaj Saleem Kazmi, Muhammad Junaid Munir","doi":"10.1016/j.susmat.2024.e01149","DOIUrl":null,"url":null,"abstract":"<div><div>The sustainability of structural components requires recycled aggregate concrete (RC) to achieve adequate flexural and split tensile strengths for practical use. These strengths depend on mix design and the properties of recycled coarse aggregates (R-CA). The variability in R-CA sources and the inherent heterogeneity of RC complicate strength predictions. No existing model accounts for this variability, leaving a gap between sustainable materials and structural design. This study develops machine learning-based models to predict the flexural and split tensile strengths of RC, regardless of R-CA source and properties. Key factors such as water absorption, effective water-to-cement ratio, coarse aggregate-to-cement ratio, and R-CA replacement ratio are used for predictions. The impact of different R-CA types on RC and natural aggregate concrete (NC) is also experimentally analyzed. A dataset of 353 test results from this study and 33 prior studies is used, and various machine learning algorithms (MLA) are evaluated. Results show a 41 % and 23 % reduction in flexural and split tensile strengths of RC compared to NC, but acid and mechanically treated R-CA can recover up to 94 % and 93 % of NC's strengths, respectively. Among all MLA models, the gradient boost model depicted the highest accuracy in predictions for the flexural and tensile strengths of both RC and NC. This research introduces new equations and a C/C++ tool for predicting RC and NC strengths, contributing to sustainable concrete design and bridging the gap between research and practical application.</div></div>","PeriodicalId":22097,"journal":{"name":"Sustainable Materials and Technologies","volume":"42 ","pages":"Article e01149"},"PeriodicalIF":8.6000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Experimental investigation and machine learning models for predicting flexural and tensile strengths of recycled concrete: Bridging the gap between sustainable materials and structural design\",\"authors\":\"Syed Minhaj Saleem Kazmi, Muhammad Junaid Munir\",\"doi\":\"10.1016/j.susmat.2024.e01149\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The sustainability of structural components requires recycled aggregate concrete (RC) to achieve adequate flexural and split tensile strengths for practical use. These strengths depend on mix design and the properties of recycled coarse aggregates (R-CA). The variability in R-CA sources and the inherent heterogeneity of RC complicate strength predictions. No existing model accounts for this variability, leaving a gap between sustainable materials and structural design. This study develops machine learning-based models to predict the flexural and split tensile strengths of RC, regardless of R-CA source and properties. Key factors such as water absorption, effective water-to-cement ratio, coarse aggregate-to-cement ratio, and R-CA replacement ratio are used for predictions. The impact of different R-CA types on RC and natural aggregate concrete (NC) is also experimentally analyzed. A dataset of 353 test results from this study and 33 prior studies is used, and various machine learning algorithms (MLA) are evaluated. Results show a 41 % and 23 % reduction in flexural and split tensile strengths of RC compared to NC, but acid and mechanically treated R-CA can recover up to 94 % and 93 % of NC's strengths, respectively. Among all MLA models, the gradient boost model depicted the highest accuracy in predictions for the flexural and tensile strengths of both RC and NC. This research introduces new equations and a C/C++ tool for predicting RC and NC strengths, contributing to sustainable concrete design and bridging the gap between research and practical application.</div></div>\",\"PeriodicalId\":22097,\"journal\":{\"name\":\"Sustainable Materials and Technologies\",\"volume\":\"42 \",\"pages\":\"Article e01149\"},\"PeriodicalIF\":8.6000,\"publicationDate\":\"2024-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Materials and Technologies\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214993724003294\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Materials and Technologies","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214993724003294","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Experimental investigation and machine learning models for predicting flexural and tensile strengths of recycled concrete: Bridging the gap between sustainable materials and structural design
The sustainability of structural components requires recycled aggregate concrete (RC) to achieve adequate flexural and split tensile strengths for practical use. These strengths depend on mix design and the properties of recycled coarse aggregates (R-CA). The variability in R-CA sources and the inherent heterogeneity of RC complicate strength predictions. No existing model accounts for this variability, leaving a gap between sustainable materials and structural design. This study develops machine learning-based models to predict the flexural and split tensile strengths of RC, regardless of R-CA source and properties. Key factors such as water absorption, effective water-to-cement ratio, coarse aggregate-to-cement ratio, and R-CA replacement ratio are used for predictions. The impact of different R-CA types on RC and natural aggregate concrete (NC) is also experimentally analyzed. A dataset of 353 test results from this study and 33 prior studies is used, and various machine learning algorithms (MLA) are evaluated. Results show a 41 % and 23 % reduction in flexural and split tensile strengths of RC compared to NC, but acid and mechanically treated R-CA can recover up to 94 % and 93 % of NC's strengths, respectively. Among all MLA models, the gradient boost model depicted the highest accuracy in predictions for the flexural and tensile strengths of both RC and NC. This research introduces new equations and a C/C++ tool for predicting RC and NC strengths, contributing to sustainable concrete design and bridging the gap between research and practical application.
期刊介绍:
Sustainable Materials and Technologies (SM&T), an international, cross-disciplinary, fully open access journal published by Elsevier, focuses on original full-length research articles and reviews. It covers applied or fundamental science of nano-, micro-, meso-, and macro-scale aspects of materials and technologies for sustainable development. SM&T gives special attention to contributions that bridge the knowledge gap between materials and system designs.