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

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 appl...