## 2 Literature Review  
### Machine Learning Models for Prediction  
In recent years, there has been an increasing number of studies using ML to predict the strength of geopolymers. Among these, artificial neural networks (ANNs) have emerged as the most prominent, with numerous studies demonstrating promising prediction capabilities [14; 15; 16]. Alongside ANNs, convolutional neural networks (CNNs) [17] and deep neural networks (DNNs) [18] are also utilized. Additionally, tree-based algorithms including decision tree (DT) [19], random forest (RF) [20], LightGBM [20], and MP5-tree [21] are employed, showing advantages in model interpretation and handling skewed data. Similarly, recently emerged algorithms known as gene expression programming (GEP) [22] and its variants exhibit superior model interpretation capabilities compared to neural network models. SVM [15] is another popular algorithm that yields results comparable to ANN. To further enhance model performance, various optimization algorithms such as grey wolf optimizer (GWO) [23] and particle swarm optimization (PSO) [23] are employed. Additionally, stacked and ensemble models are utilized in several studies, demonstrating improved performance compared to individual models. The input variables in these studies typically include mixture proportions, chemical compositions of precursors, specimen age, and external factors such as curing temperature and curing regimes. Table 1 presents the inputs, dataset sizes, and algorithms used in some related studies. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Refs.** & **ML Technology** & **Dataset** & **Inputs** \\ \hline
[24] & LR, EL, SVMR, GPR, optimized EL, optimized SVR, optimized GPR & 275 & Fly ash, coarse aggregate, fine aggregate, Na\({}_{2}\)SiO\({}_{3}\), NaOH, SiO\({}_{2}\), molarity of NaOH and Na\({}_{2}\)O \\ \hline
[19] & DT, AdaBoost, RF & 363 & Water/solids ratio, molarity of NaOH, gravel 4/10 mm, gravel 10/20 mm, Na\({}_{2}\)SiO\({}_{3}\), NaOH, fly ash, GGBS, fag \\ \hline
[20] & RF, GB, AdaBoost, DT, lightGBM, XGB, kNN, MVR, GPR, CatB & 158 & Fly ash, superplasticizer, extra water added, Na\({}_{2}\)SiO\({}_{3}\), NaOH, coarse aggregate, fine aggregate, slag, specimen age, rest period, curing temperature, molarity, alkali/binder, and Na\({}_{2}\)SiO\({}_{3}\)/NaOH \\ \hline
[14] & ANN, MLR, MNLR & 289 & Fly ash, slag, rest period, curing temperature, age, NaOH/Na\({}_{2}\)SiO\({}_{3}\), superplasticizer, extra water, molarity of NaOH, alkali/binder, coarse aggregate, fine aggregate \\ \hline
[15] & SVM, DT, RFR & 75 & Molarity of NaOH, curing temperature, age, \% of Na\({}_{2}\)SiO\({}_{3}\), NaOH, fly ash, coarse aggregate, fine aggregate, Na\({}_{2}\)SiO\({}_{3}\), NaOH \\ \hline
[25] & ANN, MSP, LR, MLR & 220 & Alkali/binder, fly ash, Si/Al of fly ash, GGBS, Si/Ca of GGBS, coarse aggregate, fine aggregate, Na\({}_{2}\)SiO\({}_{3}\), NaOH, Na\({}_{2}\)SiO\({}_{3}\)/NaOH, molarity of NaOH \\ \hline
[26] & ANN, MARS & 208 & Water/solid ratio, alkali/binder, Na-Silicate/NaOH, fly ash/slag ratio, NaOH molarity \\ \hline
[27] & RF, GBR, AdaBoost & 321 & GGBS, Na\({}_{2}\)SiO\({}_{3}\) + NaOH (total alkali), coarse aggregate, fine aggregate, water, water/binder, age \\ \hline
[23] & LR, GA, PSO, SVR, GWO, Differential Evolution & 268 & Water, curing temperature, water/binder, GGBFS/ binder, coarse aggregate, superplasticizer \\ \hline
[28] & ANN & 81 & GGBS, silica fume \\ \hline
[16] & ANN, BLR & 625 & Na\({}_{2}\)O-SiO\({}_{2}\), Na\({}_{2}\)O:H\({}_{2}\)O, GGBFS:H\({}_{2}\)O, molarity of NaOH, Na\({}_{2}\)SiO\({}_{3}\)/NaOH, AS/GGBS, GGBS/water \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical description for the features in the dataset. Table 1 indicates that neural network algorithms, tree-based models, and support vector algorithms are of significant interest. Furthermore, it is observed that the sample size in the majority of studies is insufficient for ML models, particularly when utilizing neural networks and deep learning.