Comparative analysis of various machine learning algorithms to predict 28-day compressive strength of Self-compacting concrete

Waleed Bin Inqiad; Muhammad Shahid Siddique; Saad S Alarifi; Muhammad Jamal Butt; Taoufik Najeh; Yaser Gamil

doi:10.1016/j.heliyon.2023.e22036

Comparative analysis of various machine learning algorithms to predict 28-day compressive strength of Self-compacting concrete

Heliyon. 2023 Nov 8;9(11):e22036. doi: 10.1016/j.heliyon.2023.e22036. eCollection 2023 Nov.

Authors

Waleed Bin Inqiad¹, Muhammad Shahid Siddique¹, Saad S Alarifi², Muhammad Jamal Butt³, Taoufik Najeh⁴, Yaser Gamil⁵

Affiliations

¹ Military College of Engineering (MCE), National University of Science and Technology (NUST), Islamabad 44000, Pakistan.
² Department of Geology and Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia.
³ COMSATS University Islamabad, Abbottabad Campus, Pakistan.
⁴ Operation and Maintenance, Operation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Sweden.
⁵ Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia.

Abstract

Construction industry is indirectly the largest source of ${CO}_{2}$ emissions in the atmosphere, due to the use of cement in concrete. These emissions can be reduced by using industrial waste materials in place of cement. Self-Compacting Concrete (SCC) is a promising material to enhance the use of industrial wastes in concrete. However, there are very few methods available for accurate prediction of its strength, therefore, reliable models for estimating 28-day Compressive Strength (C-S) of SCC are developed in current study by using three Machine Learning (ML) algorithms including Multi Expression Programming (MEP), Extreme Gradient Boosting (XGB), and Random Forest (RF). The ML models were meticulously developed using a dataset of 231 points collected from internationally published literature considering seven most influential parameters including cement content, quantities of fly ash and silica fume, water content, coarse aggregate, fine aggregate, and superplasticizer dosage to predict C-S. The developed models were evaluated using different statistical errors including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination ( $R^{2}$ ) etc. The results showed that the XGB model outperformed the MEP and RF model in terms of accuracy with a correlation $R^{2}$ = 0.998 compared to 0.923 for MEP and 0.986 for RF. Similar trend was observed for other error metrices. Thus, XGB is the most accurate model for estimating C-S of SCC. However, it is pertinent to mention here that it does not give its output in the form of an empirical equation like MEP model. The construction of these empirical models will help to efficiently estimate C-S of SCC for practical purposes.

Keywords: 28-Day compressive strength (C–S); Extreme gradient boosting (XGB); Machine learning (ML); Multi expression programming (MEP); Random forest (RF); Self-compacting concrete (SCC).