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Open AccessArticle
Data-Driven Predictive Modeling of Steel Slag Concrete Strength for Sustainable Construction
by
Asad S. Albostami
Asad S. Albostami 1
,
Rwayda Kh. S. Al-Hamd
Rwayda Kh. S. Al-Hamd 2,*
and
Ali Ammar Al-Matwari
Ali Ammar Al-Matwari 1
1
School of Engineering & Construction, Oryx Universal College in Partnership with Liverpool John Moores, Doha P.O. Box 12253, Qatar
2
School of Applied Sciences, Abertay University, Dundee DD1 1HQ, UK
*
Author to whom correspondence should be addressed.
Buildings 2024, 14(8), 2476; https://doi.org/10.3390/buildings14082476 (registering DOI)
Submission received: 2 July 2024
/
Revised: 26 July 2024
/
Accepted: 7 August 2024
/
Published: 10 August 2024
Abstract
Conventional concrete causes significant environmental problems, including resource depletion, high CO2 emissions, and high energy consumption. Steel slag aggregate (SSA), a by-product of the steelmaking industry, offers a sustainable alternative due to its environmental benefits and improved mechanical properties. This study examined the predictive power of four modeling techniques—Gene Expression Programming (GEP), an Artificial Neural Network (ANN), Random Forest Regression (RFR), and Gradient Boosting (GB)—to predict the compressive strength (CS) of SSA concrete. Using 367 datasets from the literature, six input variables (cement, water, granulated furnace slag, superplasticizer, coarse aggregate, fine aggregate, and age) were utilized to predict compressive strength. The models’ performance was evaluated using statistical measures such as the mean absolute error (MAE), root mean squared error (RMSE), mean values, and coefficient of determination (R2). Results indicated that the GB model consistently outperformed RFR, GEP, and the ANN, achieving the highest R2 values of 0.99 and 0.96 for the training and testing dataset, respectively, followed by RFR with R2 values of 0.97 (training) and 0.93 (testing), GEP with R2 values of 0.85 (training) and 0.87 (testing), and ANN with R2 values of 0.61 (training) and 0.82 (testing). Additionally, the GB model had the lowest MAE values of 0.79 MPa (training) and 2.61 MPa (testing) and RMSE values of 1.90 MPa (training) and 3.95 MPa (testing). This research aims to advance predictive modeling in sustainable construction through thorough analysis and well-defined conclusions.
Share and Cite
MDPI and ACS Style
Albostami, A.S.; Al-Hamd, R.K.S.; Al-Matwari, A.A.
Data-Driven Predictive Modeling of Steel Slag Concrete Strength for Sustainable Construction. Buildings 2024, 14, 2476.
https://doi.org/10.3390/buildings14082476
AMA Style
Albostami AS, Al-Hamd RKS, Al-Matwari AA.
Data-Driven Predictive Modeling of Steel Slag Concrete Strength for Sustainable Construction. Buildings. 2024; 14(8):2476.
https://doi.org/10.3390/buildings14082476
Chicago/Turabian Style
Albostami, Asad S., Rwayda Kh. S. Al-Hamd, and Ali Ammar Al-Matwari.
2024. "Data-Driven Predictive Modeling of Steel Slag Concrete Strength for Sustainable Construction" Buildings 14, no. 8: 2476.
https://doi.org/10.3390/buildings14082476
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