Modeling of mechanical properties of silica fume-based green concrete using machine learning techniques

Nafees A. Amin M.N. Khan K. Nazir K. Ali M. Javed M.F. Aslam F. Musarat M.A. Vatin N.I.
January-1 2022 MDPI

Polymers
2022 #14 Issue 1

Silica fume (SF) is a frequently used mineral admixture in producing sustainable concrete in the construction sector. Incorporating SF as a partial substitution of cement in concrete has ob-vious advantages, including reduced CO₂ emission, cost-effective concrete, enhanced durability, and mechanical properties. Due to ever-increasing environmental concerns, the development of predictive machine learning (ML) models requires time. Therefore, the present study focuses on developing modeling techniques in predicting the compressive strength of silica fume concrete. The employed techniques include decision tree (DT) and support vector machine (SVM). An extensive and reliable database of 283 compressive strengths was established from the available literature information. The six most influential factors, i.e., cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume, were considered as significant input parameters. The evaluation of models was performed by different statistical parameters, such as mean absolute error (MAE), root mean squared error (RMSE), root mean squared log error (RMSLE), and coefficient of determination (R²). Individual and ensemble models of DT and SVM showed satisfactory results with high prediction accuracy. Statistical analyses indicated that DT models bested SVM for predicting compressive strength. Ensemble modeling showed an enhancement of 11 percent and 1.5 percent for DT and SVM compressive strength models, respectively, as depicted by statistical parameters. Moreover, sensitivity analyses showed that cement and water are the governing parameters in developing compressive strength. A cross-validation technique was used to avoid overfitting issues and confirm the generalized modeling output. ML algorithms are used to predict SFC compressive strength to promote the use of green concrete.

Cross-validation , Green concrete , Industrial waste , Machine learning , Predictive modeling , Sensitivity analysis

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Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, Pakistan
Department of Civil and Environmental Engineering, College of Engineering, King Faisal University (KFU), P.O. Box 380, Al Ahsa, Al-Hofuf, 31982, Saudi Arabia
Department of Civil Engineering, School of Engineering, Nazabayev University, Astana, 010000, Kazakhstan
Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Malaysia
Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
Institute of Civil Engineering, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, 195291, Russian Federation

Department of Civil Engineering
Department of Civil and Environmental Engineering
Department of Civil Engineering
Department of Civil and Environmental Engineering
Department of Civil Engineering
Institute of Civil Engineering

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