## 5 Experimental Programme  
### Estimative Performance of ANN, DNN, and ResNet Approaches  
In the first phase, six predictive models based on three proposed machine learning approaches (ANN, DNN, and ResNet) were trained and validated using randomly shuffled datasets obtained from experimental works. Input variables in the dataset consisted of six parameters including mixture proportions (i.e., NaOH/Na\({}_{2}\)SiO\({}_{3}\), fly ash/aggregate, AL/fly ash), NaOH concentration, and curing conditions. Compressive strength of FAGP specimens was regarded as output variable. The results from the first phase were aimed to provide a short list of models to further test with K-fold cross validation and the \(t\)-test method as described in Section 4. R\({}^{2}\), RMSE, and MAPE values for the ANN (architecture 1 and 2), DNN (architecture 3 to 4), and ResNet (architecture 5 to 6) models are summarised in Table 4, with the bold numbers representing the best predictive model of each approach. As shown, architectures 1, 3, and 6 were found to be the best ANN, DNN, and ResNet models, respectively. From the six architectures presented in Table 4, ResNet-based architecture 6 was the best model for determining FAGP concrete compressive strength with the highest R\({}^{2}\) of 0.937 and lowest RMSE and MAPE values (1.987 and 6.6, respectively). Apart from the ResNet models, ANN-based architecture 1 (R\({}^{2}\) = 0.889; RMSE = 4.711; MAPE = 14.06) and DNN-based architecture 3 (R\({}^{2}\) = 0.898; RMSE = 2.521; MAPE = 9.496) showed better predictive performance than the other models (architecture 2, 4, and 5). Based on these observations, ANN-based architecture 1, DNN-based architecture 3, and ResNet-based architecture 6 were selected for further investigation. In the second phase, further investigation into performance of the proposed approaches was carried out using additional training and assessment under a 10-fold scheme with three architectures: 1, 3, and 6. Results of various statistic measures (R\({}^{2}\), MAPE, and RMSE) for each fold and the average (Avg.) values with standard deviations are presented in Table 5. The same training and validation sets of each fold were applied for the three models 1, 3, and 6. As shown in this table, ResNet-based architecture 6 obtained the best strength prediction performance in terms of R\({}^{2}\) (0.934 \(\pm\) 0.021), RMSE (2.750 \(\pm\) 0.573), and MAPE (8.552 \(\pm\) 1.333. Also, a further paired \(t\)-test with \(\alpha\) = 0.05 was applied to prove the statistical significance of this observation. As presented in Table 6, \(p\)-values from the comparisons of ResNet model and ANN/DNN models were lower than the chosen significance level (\(\alpha\) = 0.05), providing statistical confirmation that the ResNet model out-performed the ANN/DNN model in terms of FAGP strength prediction. Relationships between the experimental and predicted strength values from architectures 1, 3, and 6 are illustrated in Figure 6. As shown, compressive strength values predicted by all machine learning models were close to the actual values obtained from compression experiments, indicating that the proposed approaches were successfully trained to predict FAGP compressive strength. The ResNet model outperformed the other models with the strongest relationship existing between actual and predicted values. This observation was confirmed in Figure 7, which presents the correlation coefficient (R) of the three approaches in terms of validation data. Minimal variation existed between actual and predicted values existed for the ANN, DNN, and ResNet models, albeit with the highest variance being associated with the former (architecture 1). The relationship between iterations of the three best performed architectures (1, 3, and 6) and validation RMSE is shown in Figure 8. The highest convergence speed was observed in ResNet-based architecture 6 model, which required only 2000 iterations to reach a validation RMSE of 4.8 MPa.