## 6 Conclusions  
This study employed three different machine learning approaches including ANN, DNN, and ResNet to predict compressive strength of fly ash-based geopolymer concrete. Six parameters of mix design and curing conditions (including NaOH/Na\({}_{2}\)SiO\({}_{3}\), fly ash/aggregate, AL/Fly ash, concentration of sodium hydroxide, curing time, and temperature) and corresponding 7-day compressive strength results were used to generate 263 unique input/output pairs for model training purposes.  
While the results indicated that all three machine learning approaches could predict FAGP concrete compressive strength with some degree of accuracy, the ResNet model was the most promising method with the highest R\({}^{2}\) (0.937) and the lowest RMSE (1.987) and MAPE (6.6) values. This observation was confirmed by additional training and assessment under the K-fold cross validation scheme and paired \(t\)-test with \(\alpha\) = 0.05, where the highest R\({}^{2}\) (0.934 \(\pm\) 0.021) and the lowest RMSE (2.750 \(\pm\) 0.573) and MAPE (8.552 \(\pm\) 1.333) were observed in the ResNet-based model. Sensitivity analysis performed for the ResNet model confirmed that the ratio of fly ash/aggregate was the most dominant factor when predicting compressive strength, with a sensitivity analysis score of 35%. This was followed in order of importance by curing temperature (16.22%), NaOH concentration (16.18%), curing time (14.93%), NaOH/Na\({}_{2}\)SiO\({}_{3}\) ratio (12.90%), and AL/fly ash ratio (4.22%). This analysis indicates the importance of considering a wide range of input parameters in the prediction of FAGP concrete compressive strength and controlling them carefully during the manufacturing process.  
This study provides a detailed understanding of performance of different machine learning approaches in strength prediction for FAGP concrete. The findings highlight potential uses of the proposed machine learning approaches such as ResNet and DNN as effective tools to, not only precisely  
Figure 10: Sensitivity analysis parameters for the estimated compressive strength of FAGP concrete.  
predict mechanical properties of FAGP, but also to develop mix designs for geopolymer concrete. In the next phase of work, consideration will be given to how ResNet and DNN models can be applied in FAGP manufacturing industries to predict optimised mix designs and curing regimes based on target compressive strength. This predictive ability will also be linked to mix design evaluations in terms of potential cost and environmental benefits prior to construction stages. In addition, the proposed machine learning approaches adopted a general training scheme for neural networks with standard input and output features, indicating promising potential to be applied to other regression problems in upcoming research works.  
Conceptualization, Q.D.N. and K.T.N.; Data curation, Q.D.N. and Q.L.X.; Funding acquisition, T.C.; Investigation, A.T.H., Q.D.N. and K.T.N.; Methodology, Q.D.N. and Q.L.X.; Project administration, K.T.N.; Resources, K.T.N.; Software, Q.D.N. and Q.L.X.; Supervision, B.M. and T.C.; Validation, Q.L.X. and T.C.; Visualization, K.T.N.; Writing--original draft, A.T.H. and K.T.N.; Writing--review & editing, A.T.H., Q.L.X., K.T.T and B.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic Science Research Program through the National Research Foundation of Korea (NRF-2020R1F1A1050014).  
The authors declare no conflict of interest.