## 1 Introduction  
Concrete stands as the most extensively utilized man-made material worldwide [1]. The incessantly rising demand for concrete translates into a parallel surge in the need for cement, evidenced by a reported 12% increase in 2019 and an anticipated doubling by 2050 [2]. Nonetheless, the production of cement imposes diverse environmental repercussions [2]. Each ton of cement manufactured emits 0.6-1 ton of CO\({}_{2}\), contingent upon production methods, contributing 5-7% to global CO\({}_{2}\) emissions [1]. Additionally, limestone exploitation for cement production triggers water and land pollution, disturbing local ecosystems and biodiversity [3]. Therefore, numerous studies have been conducted to explore alternative materials to replace cement. In recent years, alkali-activated materials, such as geopolymers, have emerged as a promising alternative to OPC-based materials due to their lower carbon footprint and utilization of industrial by-products [4]. Geopolymers are polymeric aluminosilicate cementitious materials that involve industrial wastes (i.e., fly ash, slag, and silica fume) as the precursors under the action of alkaline chemicals such as sodium hydroxide and sodium silicate [5]. Similar to OPC concrete, the strength of geopolymer concrete is influenced by various parameters, including both external and internal factors. External factors such as temperature, duration, curing types, humidity, and air containment play significant roles in determining the strength of geopolymer concrete [6]. On the other hand, internal factors, such as material quality and mixture compositions, also have a substantial impact. Because of the intricate relationships and inconsistencies in precursors and alkaline activators, a mixture proportion can yield a wide range of compressive strengths [7]. Therefore, calculating the distribution and predicting the intensity using empirical and theoretical methods pose significant challenges. With the growth in ML applications, researchers are increasingly turning to ML to address the problem of compressive strength prediction for geopolymer concrete [8]. ML models heavily rely on large, high-quality datasets and models trained on datasets containing fewer than 1000 data points are prone to overfitting problems [9]. However, according to the research of [PERSON] et al. (2022) [9], only 11% of the studies on applying ML to concrete science utilize datasets containing more than 1000 samples. Also, the average sample size is estimated to be 174 [9]. To mitigate the challenge of data scarcity in concrete science, several studies have adopted generative models such as Generative Adversarial Networks (GANs) [10], Conditional Generative Adversarial Networks (CGANs) [11], Cycle-Consistent Deep Generative Adversarial Networks (CDGANs) [11], and TGANs [12; 13] to generate synthetic data for ML models. The use of synthetic data has shown significant improvements in model performance, particularly noticeable with TGAN and CDGAN models [11; 13]. However, existing research predominantly focuses on generating data for OPC concrete with less emphasis on geopolymer concrete. Meanwhile, geopolymer mixtures involve a more intricate ingredient system including alumino-silicate precursors and alkali activators, which are more complex than the cement-water system of OPC concrete [5]. Moreover, geopolymer precursors exhibit significant variations in chemical composition and reactivity across different factories, regions, and time periods [5]. Conversely, cement exhibits greater standardization in these aspects, resulting in more consistent data collection and synthesis. Consequently, the impact on generating and utilizing synthetic data on geopolymer strength prediction models may differ from that of OPC concrete. However, research focusing on the effectiveness of data augmentation specifically for geopolymer concrete remains limited.