Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

• Published in: Polymers Vol. 14; no. 6; p. 1074
• Main Authors: Wang, Qichen, Ahmad, Waqas, Ahmad, Ayaz, Aslam, Fahid, Mohamed, Abdullah, Vatin, Nikolai Ivanovich
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.03.2022; MDPI
• Subjects: Aggregates; Algorithms; Aluminosilicates; Aluminum silicates; Artificial intelligence; Cement; Composite materials; Compressive strength; Concrete; Construction industry; Decision trees; Errors; Gene expression; Geopolymers; Landfill; Machine learning; Material properties; Portland cements; R&D; Research & development; Soft computing; Statistical tests; Temperature effects; Variables; Waste materials; compressive strength; prediction models; geopolymer composites; machine learning; sustainable materials; artificial intelligence
• DOI: 10.3390/polym14061074
• ISSN: 2073-4360

Geopolymers may be the best alternative to ordinary Portland cement because they are manufactured using waste materials enriched in aluminosilicate. Research on geopolymer composites is accelerating. However, considerable work, expense, and time are needed to cast, cure, and test specimens. The application of computational methods to the stated objective is critical for speedy and cost-effective research. In this study, supervised machine learning approaches were employed to predict the compressive strength of geopolymer composites. One individual machine learning approach, decision tree, and two ensembled machine learning approaches, AdaBoost and random forest, were used. The coefficient correlation (R2), statistical tests, and k-fold analysis were used to determine the validity and comparison of all models. It was discovered that ensembled machine learning techniques outperformed individual machine learning techniques in forecasting the compressive strength of geopolymer composites. However, the outcomes of the individual machine learning model were also within the acceptable limit. R2 values of 0.90, 0.90, and 0.83 were obtained for AdaBoost, random forest, and decision models, respectively. The models’ decreased error values, such as mean absolute error, mean absolute percentage error, and root-mean-square errors, further confirmed the ensembled machine learning techniques’ increased precision. Machine learning approaches will aid the building industry by providing quick and cost-effective methods for evaluating material properties.