## 4 Research Methodology  
In this study, 263 datasets (each comprising six inputs and one output) were used to train and validate ANN, DNN, and ResNet models. In terms of inputs, each dataset comprised a unique combination of the six mix design values considered, as summarised in Table 2. The output for each dataset was the corresponding average compressive 7-day strength result obtained from experimental testing. The range of strength values recorded for the 263 combinations considered was 5.55-67.86 MPa. A data division scheme was applied to reduce possibilities of error and improve the reliability of predicted results. Random selection of about 90% of the values (235 datasets) in the training dataset were chosen from the original data collection to train the network, while the remaining values (28 datasets) remained untrained as a validation database to confirm the accuracy of the trained network. The structures of three machine learning approaches including ANN, DNN, and ResNet are presented in the schematic flowchart in Figure 5.  
FAGP concrete compressive strength was predicted by employing ANN, DNN, and ResNet architectures comprising weight, normalisation, and activation layers in regression tasks. For comparative purposes, DNN and ResNet models consisted of the same number of nodes with 128 nodes in Weight Layer 1 and 256 nodes in Weight Layer 2. A layer with 256 nodes, known as Weight Layer 3, was included to enable additional operation at the end of ResNet implementation. The ANN model with one weight layer comprised 384 nodes. One of the stochastic gradient descent methods, known as Adam [35], was used as the optimisation method to update neural networks coefficients since it integrated advanced features from different optimisation algorithms, including AdaGrad and RMSProp. The layer normalisation method introduced by [PERSON] et al. [36] was employed to ensure inputs to layers fell within specific ranges since it exhibited efficient training time in neural network architecture compared to traditional batch normalisation. Training models without normalisation were also carried out to validate the effectiveness of the model integrated with layer normalisation. During the training process, dropping out units with keep probability of 0.2 in the architectures were included in the final models to prevent overfitting problems. Table 3 presents details of the setting of six architectures (known as architectures 1-6) implemented in this study. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Unit** & **Value** & **Variable** \\ \hline NaOH/Na\({}_{2}\)SiO\({}_{3}\) & & Min. 0.4–Max. 2.5 & \\ Fly ash/Aggregate & & Min. 0.13–Max. 0.37 & \\ AL/Fly ash & & Min. 0.3–Max. 0.7 & \\ NaOH concentration & M & 4, 8, 11, 12, 15, 18 & Input \\ Curing time & h & 2, 4, 6, 8, 10 & \\ Curing temperature & \({}^{\circ}\)C & 40, 60, 80, 90, 100, 120 & \\ Compressive strength & MPa & 5.44–67.86 & Output \\ Total number of datasets & & 263 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical parameters of fly ash-based geopolymer (FAGP) concrete used in the training dataset. Three statistical measures including R\({}^{2}\), RMSE, and MAPE were applied to evaluate the accuracy of the proposed machine learning approaches under the K-fold cross validation scheme. These parameters provide insights into differences between original and estimated values. Higher R\({}^{2}\) value and/or lower MAPE and RMSE values indicate better prediction performance of machine learning approaches [19].