## 4 Results and Discussion  
### Data Collection and Preparation  
This study utilized 930 data points derived from laboratory experiments to develop and test prediction models. The data, extracted from peer-reviewed articles [41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59], pertained to mixtures and compressive strength of alkali-activated concrete using fly ash and slag. The data were manually curated from text, tables, and figures within the publications. The dataset comprises 10 input variables and 1 output as depicted in Table 3. Notably, natural aggregates served as both coarse and fine aggregates for the model. Additionally, the concrete specimen sizes varied across articles according to adopted standards, namely cube size (150 mm) and cylinder size (150 mm \(\times\) 300 mm, 100 mm \(\times\) 200 mm). Since the size of the testing sample influences compressive strength, all data points were standardized to a consistent size (150 mm \(\times\) 300 mm cylinder) using the Elwel table [60]. To understand the details of the variables in the collected dataset, a statistical analysis was performed with IBM SPSS Statistics 27.0. Based on the initial analysis, the distributions of the SP and CP were skewed and contained significant numbers of outliers, which could severely reduce the performance of ML models. To address this issue, the interquartile range (IQR) method was employed to identify and remove outliers from these two parameters. The interquartile range is a robust measure of scale that is not influenced by outliers [61]. It  
Figure 3: Evaluation flowchart of data augmentation and predictive models. is calculated as the difference between the 75 th percentile (\(Q3\)) and the 25 th percentile (\(Q1\)) of the data:  
\[IQR=Q3-Q1 \tag{13}\]  
For the SP and CP parameters, the IQR was calculated, and any data point that fell outside the range of [\(Q1-1.5\times(IQR)\), \(Q3+1.5\times(IQR)\)] was identified as an outlier and removed from the dataset [61]. After removing the outliers from the SP and CP, the remaining data points in the dataset were used for further analysis and modeling. Figure 4a depicts the box plot of the dataset after outlier removal. The Pearson correlation coefficients between all pairs of input parameters were calculated and illustrated in the correlation matrix shown in Figure 4b. The heatmap indicates that there are no parameters having strong linear relationship with each other. So, all the parameters are kept to construct the train and test datasets. The statistical description of the dataset after outlier removal is presented in Table 3. The content of fly ash and GGBS varied significantly, with values ranging from 0 to 620 kg/m\({}^{3}\) and 560 kg/m\({}^{3}\), respectively. This wide range is attributed to the dataset including mixtures utilizing solely FA or GGBS, as well as combinations of both materials. The age of the concrete ranged from 1 to 56 days and the compressive strength ranged from 1.25 to 80.51 MPa. In summary, the analysis of box plots, correlation heatmaps, and various statistical parameters reveals that the dataset contains few outliers and can accommodate all 10 input variables without multicollinearity issues. However, certain variables exhibit high skewness, indicating non-normal data distribution. This deviation from normality contradicts the assumptions of classical ML algorithms [62] like linear regression and Gaussian Naive Bayes. Therefore, the algorithms applied should be non-parameter algorithms or neural networks which do not explicitly assume that the input features (independent variables) are normally distributed or have a symmetric distribution [62]. Figure 4: Data analysis. (**a**) Box plot after outlier removal. (**b**) Correlation matrix.