## 1 Introduction  
Emission of carbon dioxide caused by various sectors, including construction, industrial processes, transport, residential, and agriculture, has emerged as a severe problem that dramatically affectsglobal climate change. Calcining limestone in Portland cement production represents 8% of global anthropogenic CO2 emission [1]. Global production of cement increased rapidly from 1.5 billion tonnes in 1998 [2] to 4.1 billion tonnes in 2018 [3], which has significantly impacted emissions linked to the construction sector. This justifies the need for more sustainable alternatives sourced from industrial by-products/wastes with minimal embodied carbon, offering a balance of technical, environmental, and economic benefits. In this context, geopolymer concrete using industrial by-products (e.g., fly ash and ground-granulated blast-furnace slag) has been reported to reduce up to 80% of CO2 emission relative to conventional concrete [4]. Geopolymer products are synthesised through the reaction of alkali liquid with silica and alumina contained in aluminosilicate precursors. Depending upon local resources, solid aluminosilicate precursors can be sourced from industrial by-products such as fly ash, metakaolin, red mud, and waste glass [5; 6; 7; 8]. According to previous studies [9; 10; 11; 12; 13], fly ash-based geopolymer (FAGP) concrete showed the ability to achieve high compressive strengths up to 68 MPa. To assess the compressive strength of concrete, universal compression testing machines are typically used to apply compression load on cylindrical or cube specimens at a prescribed rate (e.g., 20-50 psi/s or 0.14-0.35 MPa/s based on American Society for Testing and Materials (ASTM) C39/C39M-18 [14]). In addition to direct testing, non-destructive testing methods, such as ultrasonic pulse velocity and rebound hammer, are also used to predict the compressive strength of concrete products [15; 16; 17]. However, these experimental methods rely heavily on costly equipment and time-consuming preparation of specimens. As such, artificial intelligence approaches including artificial neural network, adaptive neuro fuzzy inference, and deep learning have been employed to predict the mechanical properties of FAGP concrete by several researchers [18; 19; 20; 21]. Inspired by the biological neural system, the artificial neural network (ANN) algorithm with three neuron layers has been widely applied in different research fields such as civil engineering, biochemistry, pharmaceutics, and biology owing to its ability to learn complex relationships among values in its training patterns. [PERSON] et al. [19] investigated the compressive strength of FAGP concrete consisting of steel slag aggregates using ANN and neuro fuzzy inference approaches. Mean absolute error, R-squared, and root mean square error (RMSE) were employed to evaluate the performance of the proposed approaches. Three input parameters including sodium hydroxide (NaOH) concentration, alkali activator/fly ash ratio, and sodium hydroxide-to-sodium silicate ratio were used to predict the compressive strength of FAGP concrete. Results obtained from the ANN were in substantial agreement with experiment data. Sensitivity analysis for ANN and adaptive neuro fuzzy inference were adopted in a study by the same authors [18] to evaluate the impact of each input factor including the mass of fly ash, sodium silicate (Na\({}_{2}\)SiO\({}_{3}\)), NaOH, and water on the accuracy of the proposed models. The two approaches effectively predicted the compressive strength of the geopolymer concrete using only three input parameters in the mixture proportion. Curing condition factors were neglected in these studies even though they undoubtedly play essential roles in the compressive strength of geopolymer concrete [22; 23; 24; 25]. In addition to mixture proportion factors, curing time and temperature values were added in the training dataset for compressive strength prediction of FAGP concrete using ANN in a study by [PERSON] et al. [21].