Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches

• Published in: Buildings (Basel) Vol. 12; no. 7; p. 914
• Main Authors: Shah, Syyed Adnan Raheel, Azab, Marc, Seif ElDin, Hany M., Barakat, Osama, Anwar, Muhammad Kashif, Bashir, Yasir
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2022
• Subjects: Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; Blast furnace practice; Blast furnace slags; Carbon dioxide; Cement; Composite materials; Compressive strength; Concrete; Concrete properties; Datasets; Decision making; Decision trees; Environmental impact; Fly ash; green and sustainable concrete; high performance concrete; industrial wastes; Learning algorithms; Machine learning; Neural networks; Prediction models; reinforced concrete; Reliability analysis; Slag; Statistical analysis; sustainability; Variables; Waste utilization
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings12070914

The utilization of waste industrial materials such as Blast Furnace Slag (BFS) and Fly Ash (F. Ash) will provide an effective alternative strategy for producing eco-friendly and sustainable concrete production. However, testing is a time-consuming process, and the use of soft machine learning (ML) techniques to predict concrete strength can help speed up the procedure. In this study, artificial neural networks (ANNs) and decision trees (DTs) were used for predicting the compressive strength of the concrete. A total of 1030 datasets with eight factors (OPC, F. Ash, BFS, water, days, SP, FA, and CA) were used as input variables for the prediction of concrete compressive strength (response) with the help of training and testing individual models. The reliability and accuracy of the developed models are evaluated in terms of statistical analysis such as R2, RMSE, MAD and SSE. Both models showed a strong correlation and high accuracy between predicted and actual Compressive Strength (CS) along with the eight factors. The DT model gave a significant relation to the CS with R2 values of 0.943 and 0.836, respectively. Hence, the ANNs and DT models can be utilized to predict and train the compressive strength of high-performance concrete and to achieve long-term sustainability. This study will help in the development of prediction models for composite materials for buildings.