Experimental and Informational Modeling Study of Sustainable Self-Compacting Geopolymer Concrete

Iman Faridmehr, Moncef L. Nehdi, Ghasan Fahim Huseien, Mohammad Hajmohammadian Baghban, Abdul Rahman Mohd Sam and Hassan Amer Algaifi
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Iman Faridmehr: Institute of Architecture and Construction, South Ural State University, 454080 Chelyabinsk, Russia
Moncef L. Nehdi: Department of Civil and Environmental Engineering, Western University, London, ON N6G 5L1, Canada
Ghasan Fahim Huseien: UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor 81310, Malaysia
Mohammad Hajmohammadian Baghban: Department of Manufacturing and Civil Engineering, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, Norway
Abdul Rahman Mohd Sam: UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor 81310, Malaysia
Hassan Amer Algaifi: Faculty of Civil Engineering and Built Environment, Universiti Tun Hussein Onn Malaysia, Johor 86400, Malaysia

Sustainability, 2021, vol. 13, issue 13, 1-23

Abstract: Self-compacting concrete (SCC) became a strong candidate for various construction applications owing to its excellent workability, low labor demand, and enhanced finish-ability, and because it provides a solution to the problem of mechanical vibration and related noise pollution in urban settings. However, the production of Portland cement (PC) as a primary constituent of SCC is energy-intensive, contributing to about 7% of global carbon dioxide (CO 2 ) emissions. Conversely, the use of alternative geopolymer binders (GBs) in concrete can significantly reduce the energy consumption and CO 2 emissions. In addition, using GBs in SCC can produce unique sustainable concrete with unparallel engineering properties. In this outlook, this work investigated the development of some eco-efficient self-compacting geopolymer concretes (SCGCs) obtained by incorporating different dosages of fly ash (FA) and ground blast furnace slag (GBFS). The structural, morphological, and mechanical traits of these SCGCs were examined via non-destructive tests like X-ray diffraction (XRD) and scanning electron microscopy (SEM). The workability and mechanical properties of six SCGC mixtures were examined using various measurements, and the obtained results were analyzed and discussed. Furthermore, an optimized hybrid artificial neural network (ANN) coupled with a metaheuristic Bat optimization algorithm was developed to estimate the compressive strength (CS) of these SCGCs. The results demonstrated that it is possible to achieve appropriate workability and mechanical strength through 50% partial replacement of GBFS with FA in the SCGC precursor binder. It is established that the proposed Bat-ANN model can offer an effective intelligent method for estimating the mechanical properties of various SCGC mixtures with superior reliability and accuracy via preventing the need for laborious, costly, and time-consuming laboratory trial batches that are responsible for substantial materials wastage.

Keywords: geopolymer; concrete; self-compacting; database; recycled; artificial neural network; Bat algorithm; model; prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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