Aron Berhanu Degefa, Geonyeol Jeon, Sooyung Choi, JinYeong Bak, Seunghee Park, Hyungchul Yoon, Solmoi Park
{"title":"Data-Driven Insights into Controlling the Reactivity of Supplementary Cementitious Materials in Hydrated Cement","authors":"Aron Berhanu Degefa, Geonyeol Jeon, Sooyung Choi, JinYeong Bak, Seunghee Park, Hyungchul Yoon, Solmoi Park","doi":"10.1186/s40069-024-00677-w","DOIUrl":null,"url":null,"abstract":"<p>Supplementary cementitious materials (SCMs) play an essential role in sustainable construction due to their potential to reduce carbon emissions, promote circular economy principles, and enhance the properties of concrete. However, the inherent diversity of SCMs makes it challenging to predict their degree of reaction (DOR). This study applies machine learning techniques to predict DOR while exploring key parameters affecting it. Five machine learning models are utilized: linear regression, Gaussian process regression (GPR), decision tree regression, support vector machine and extreme gradient boosting, with GPR providing the most accurate and adaptable prediction. The study delves into the impact of various parameters on DOR, revealing their significance. Silica content emerges as the most critical, followed by particle size distribution, specific gravity, and water-to-cement (W/C) ratio. Optimizing DOR requires extending curing time, reducing particle size distribution, and considering optimal silica content and W/C ratio. This research emphasizes the importance of understanding the relationships between parameters and the DOR of SCMs, providing insights to enhance the efficiency of SCMs in cementitious systems through machine learning and data-driven analysis.</p>","PeriodicalId":13832,"journal":{"name":"International Journal of Concrete Structures and Materials","volume":"39 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2024-06-28","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/s40069-024-00677-w","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
Supplementary cementitious materials (SCMs) play an essential role in sustainable construction due to their potential to reduce carbon emissions, promote circular economy principles, and enhance the properties of concrete. However, the inherent diversity of SCMs makes it challenging to predict their degree of reaction (DOR). This study applies machine learning techniques to predict DOR while exploring key parameters affecting it. Five machine learning models are utilized: linear regression, Gaussian process regression (GPR), decision tree regression, support vector machine and extreme gradient boosting, with GPR providing the most accurate and adaptable prediction. The study delves into the impact of various parameters on DOR, revealing their significance. Silica content emerges as the most critical, followed by particle size distribution, specific gravity, and water-to-cement (W/C) ratio. Optimizing DOR requires extending curing time, reducing particle size distribution, and considering optimal silica content and W/C ratio. This research emphasizes the importance of understanding the relationships between parameters and the DOR of SCMs, providing insights to enhance the efficiency of SCMs in cementitious systems through machine learning and data-driven analysis.
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.