  Citation: Barkhordari, M.S.; Armaghani, D.J.; Mohammed, A.S.; Ulrikh, D.V. Data-Driven Compressive Strength Prediction of Fly Ash Concrete Using Ensemble Learner Algorithms. Buildings 2022, 12, 132. https://doi.org/10.3390/ buildings12020132 Academic Editor: Giuseppina Uva Received: 25 December 2021 Accepted: 23 January 2022 Published: 27 January 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/). buildings Article Data-Driven Compressive Strength Prediction of Fly Ash Concrete Using Ensemble Learner Algorithms Mohammad Sadegh Barkhordari 1 , Danial Jahed Armaghani 2, * , Ahmed Salih Mohammed 3 and Dmitrii Vladimirovich Ulrikh 2 1 Department of Civil & Environmental Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 159163-4311, Iran; m.s.barkhordari@aut.ac.ir 2 Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, 454080 Chelyabinsk, Russia; ulrikhdv@susu.ru 3 Civil Engineering Department, College of Engineering, University of Sulaimani, Sulaymaniyah 46001, Iraq; ahmed.mohammed@univsul.edu.iq * Correspondence: danialarmaghani@susu.ru Abstract: Concrete is one of the most popular materials for building all types of structures, and it has a wide range of applications in the construction industry. Cement production and use have a significant environmental impact due to the emission of different gases. The use of fly ash concrete (FAC) is crucial in eliminating this defect. However, varied features of cementitious composites exist, and understanding their mechanical characteristics is critical for safety. On the other hand, for forecasting the mechanical characteristics of concrete, machine learning approaches are extensively employed algorithms. The goal of this work is to compare ensemble deep neural network models, i.e., the super learner algorithm, simple averaging, weighted averaging, integrated stacking, as well as separate stacking ensemble models, and super learner models, in order to develop an accurate approach for estimating the compressive strength of FAC and reducing the high variance of the predictive models. Separate stacking with the random forest meta-learner received the most accurate predictions (97.6%) with the highest coefficient of determination and the lowest mean square error and variance. Keywords: compressive strength; fly ash concrete; machine learning; ensemble learner algorithm; cement 1. Introduction Concrete is one of the most widely used substances in the word [1]. This is owing to the widespread usage of concrete in the buildings and civil engineering industries [2]. It is composed of a variety of elements such as coarse aggregate, fine aggregate, water, and binder, among others [3]. Its widespread use as a building material may be seen worldwide. The mechanical characteristics of concrete must be evaluated to effectively assess its performance and for use in design methods [4]. The concrete compressive strength (CCS) is treated as one of the most important parameters in the design and study of concrete structures. Because computation of the compressive strength of concrete takes a long time [5], needs a lot of material [6], and requires a lot of effort, artificial intelligence (AI) methods, as dynamic, applicable, accurate and easy-to-use technologies, have been successfully used to get around these issues [7]. Apart from these issues, AI methods have been highlighted as the main and ultimate solutions for problems in science and engineering [8,9]. Ashrafian et al. [10] used different models, including random forest (RF), M5 rule model tree, M5 prime model tree, and chi-square automatic interaction detection, for the mechanical characteristic prediction of roller-compacted concrete pavement. They concluded that RF outperformed other models. Paji et al. [11] investigated the impact of fresh and saline water on concrete samples’ compressive strength. To estimate the CCS, two Buildings 2022, 12, 132. https://doi.org/10.3390/buildings12020132 https://www.mdpi.com/journal/buildings