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

Polymers

Waqas Ahmad¹  Ayaz Ahmad¹  Fahid Aslam²  Qichen Wang³  Nikolai Ivanovich Vatin⁴  Abdullah Mohamed⁵ 

[1] Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan;Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;Department of Civil and Environmental Engineering, University of Iowa, Iowa City, IA 52242, USA;Peter the Great St. Petersburg Polytechnic University, 195291 St. Petersburg, Russia;Research Centre, Future University in Egypt, New Cairo 11745, Egypt;

关键词: geopolymer composites;    sustainable materials;    compressive strength;    artificial intelligence;    machine learning;    prediction models;   

DOI  :  10.3390/polym14061074

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown