  Citation: Wang, Q.; Ahmad, W.; Ahmad, A.; Aslam, F.; Mohamed, A.; Vatin, N.I. Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites. Polymers 2022, 14, 1074. https:// doi.org/10.3390/polym14061074 Academic Editors: Wei-Hao Lee, Yung-Ching Ding, Kae-Long Lin and Paul Joseph Received: 29 January 2022 Accepted: 26 February 2022 Published: 8 March 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). polymers Article Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites Qichen Wang 1, *, Waqas Ahmad 2, *, Ayaz Ahmad 2,3 , Fahid Aslam 4 , Abdullah Mohamed 5 and Nikolai Ivanovich Vatin 6 1 Department of Civil and Environmental Engineering, University of Iowa, Iowa City, IA 52242, USA 2 Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan; ayazahmad@cuiatd.edu.pk 3 Faculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, Poland 4 Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; f.aslam@psau.edu.sa 5 Research Centre, Future University in Egypt, New Cairo 11745, Egypt; mohamed.a@fue.edu.eg 6 Peter the Great St. Petersburg Polytechnic University, 195291 St. Petersburg, Russia; vatin@mail.ru * Correspondence: qichen-wang@hotmail.com (Q.W.); waqasahmad@cuiatd.edu.pk (W.A.) Abstract: 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 (R 2 ), 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. R 2 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. Keywords: geopolymer composites; sustainable materials; compressive strength; artificial intelli- gence; machine learning; prediction models 1. Introduction Cement-based conventional concrete (CBCC) is the most broadly utilized type of construction material on a global scale [1–3]. The primary constituents of CBCC are aggregates, water, and ordinary Portland cement (OPC) [4,5]. Following aluminum and steel, OPC is the third most energy-demanding substance on the earth, consuming 7% of the total energy of global industry [6,7]. Regrettably, the manufacture of OPC produces large quantities of greenhouse gases, i.e., CO 2 , which substantially add to climate change [8–10]. The production of OPC is anticipated to release 1.35 billion tons of greenhouse emissions annually [11–13]. Thus, scholars have focused their attempts on minimizing OPC usage through the use of alternate binder types. Alternatives to CBCC may include alkali- activated compounds such as geopolymers [14–16]. When precursors and activators react, alkali-activated compounds are formed. They have been categorized into two kinds based Polymers 2022, 14, 1074. https://doi.org/10.3390/polym14061074 https://www.mdpi.com/journal/polymers