Mohammed, A.K.; Hassan, A.M.T.; Mohammed, A.S. Predicting the Compressive Strength of Green Concrete at Various Temperature Ranges Using Different Soft Computing Techniques. Sustainability2023, 15, 11907.
Mohammed, A.K.; Hassan, A.M.T.; Mohammed, A.S. Predicting the Compressive Strength of Green Concrete at Various Temperature Ranges Using Different Soft Computing Techniques. Sustainability 2023, 15, 11907.
Mohammed, A.K.; Hassan, A.M.T.; Mohammed, A.S. Predicting the Compressive Strength of Green Concrete at Various Temperature Ranges Using Different Soft Computing Techniques. Sustainability2023, 15, 11907.
Mohammed, A.K.; Hassan, A.M.T.; Mohammed, A.S. Predicting the Compressive Strength of Green Concrete at Various Temperature Ranges Using Different Soft Computing Techniques. Sustainability 2023, 15, 11907.
Abstract
Abstract:
To overcome the environmental impact of cement production in cocnrete , the construction industry is adopting eco-friendly approaches, such as incorporating alternative and recycled materials, minimizing carbon emissions in concrete production. One such material that has gained prominence is Ground Granulated Blast Furnace Slag (GGBFS),. This study focuses on investigating the compressive strength of concrete at 28 days of age by examining the influences of several factors, such as temperature, water-to-binder ratio (w/b), GGBFS-to-binder ratio (GGBFS/b), fine aggregate, coarse aggregate, and superplasticizer. A statistical modeling approach was employed to comprehensively analyze these parameters and assess their impact on the compressive strength. To accomplish this, the study collected and analyzed data from the literature, resulting in a dataset of 210 observations. The dataset was divided into training and testing groups, and statistical analyses were performed to assess the relationships between the input parameters and compressive strength. The correlation analysis revealed insignificant relationships between the input parameters and compressive strength, indicating that multiple factors affect the strength. Different models, such as linear regression, nonlinear regression, quadratic, full quadratic models, and artificial neural networks (ANN) were employed to predict the compressive strength. The findings of this study contribute to a better understanding of the factors that influence the compressive strength of concrete containing GGBFS. The results underscore the importance of considering multiple parameters to predict strength accurately.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
