## 4 Conclusions  
The purpose of this paper was to analyze the significance of each aggregate characteristic and determine the best combinations of factors which further influence the compressive strength and elastic modulus of RAC using the ANN model. The ANN model was trained and built on the basis of a series of experimental results including 46 concrete mixes. The research took eight factors into consideration as the inputs of the ANN model. The results are as follows.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Factors** & \(\mathbf{SG}_{\mathrm{SSD}}\) + \(\mathbf{W_{a}}\) & \(\mathbf{SG}_{\mathrm{SSD}}\) + TFV & \(\mathbf{W_{a}}\) + \(\delta\) & \(\mathbf{W_{a}}\) + TFV & \(\mathbf{FM}\) + TFV & \(\mathbf{m}\) + \(\delta\) & \(\mathbf{m}\) + \(\mathbf{M_{c}}\) \\ \hline \(\mathrm{SG}_{\mathrm{SSD}}\) + \(W_{\mathrm{a}}\) & **5.94** & 5.61 & 5.43 & 5.61 & 6.26 & 6.93 & 6.55 \\ \(\mathrm{SG}_{\mathrm{SSD}}\) + TFV & & **6.11** & 6.4 & 5.61 & 6.67 & 6.85 & 5.53 \\ \(W_{\mathrm{a}}\) + \(\delta\) & & **5.76** & 5.81 & 6.33 & 6.52 & 6.16 \\ \(W_{\mathrm{a}}\) + TFV & & & **5.92** & 5.96 & 5.96 & 7.87 \\ FM + TFV & & & & **6.55** & 6.8 & 5.91 \\ \(m+\delta\) & & & & & **5.84** & 6.95 \\ \(m+M_{c}\) & & & & & **5.99** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The errors of networks for compressive strength with different input variables—Stage c (%).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factors** & \(\mathbf{M_{c}}\) & **FM** & \(\mathbf{\delta}\) & \(\mathbf{SG}_{\mathrm{SSD}}\) & \(\mathbf{W_{a}}\) \\ \hline \(\mathrm{M_{c}}\) & **6.43** & 6.93 & 6.76 & 5.5 & 5.15 \\ FM & & **6.35** & 6.98 & 5.01 & 4.94 \\ \(\mathrm{SG}_{\mathrm{SSD}}\) + \(\mathrm{T}\mathrm{F}\mathrm{V}\) + \(\mathrm{M_{c}}\) & & **5.58** & 5.76 & 5.52 \\ \(\mathrm{SG}_{\mathrm{SSD}}\) & & & **4.84** & 3.89 \\ \(\mathrm{W_{a}}\) & & & & & **4.83** \\ \hline \hline \end{tabular}
\end{table}
Table 8: The errors of networks for elastic modulus with different input variables—Stage b (%).  
1. The predicted results of RAC prepared with different sources of RAs were not satisfactory using the ANN models (\(ANN_{6}\)-\(f_{c}\) and \(ANN_{6}\)-\(E_{c}\)), although the learning abilities of these models were still good. This was because these ANN models only adopted the mix proportions and curing age as the input variables, without considering the aggregate characteristics of RA, which were quite different from the natural aggregate.
2. The water absorption of RA played a most important role in affecting the compressive strength of RAC, the addition of which could reduce the error of \(ANN_{6}\)-\(f_{c}\) from 8.11% to 6.03%. The combination of specific gravity, water absorption and impurity content could further decrease the error to only about 5.43%.
3. As regards elastic modulus, characteristics like mortar content, aggregate crushing value and 10% fines value were proved to be not important in affecting the prediction. In addition to the mix proportions and curing ages, water absorption and specific gravity were the most significant aggregate characteristics. The addition of each of them as networks inputs could decrease the error to less than 4.85%, and the network error could even be reduced to only about 3.89% when the inputs were a combination of mix proportions, curing time, specific gravity and water absorption of the coarse aggregate.  
Funding acquisition, C.-S.P. and Y.L.; Investigation, S.H. and J.X.; Methodology, Z.D. and C.-S.P.; Software, Y.L.; Writing--original draft, Z.D.  
This research was funded by the National Natural Science Foundation of China (grant number 51708419) and Shanghai Pujiang Talent Fund (grant number 17 PJ1409500).  
The authors wish to acknowledge the financial support of the Hong Kong Polytechnic University and Sun Hung Kai Properties Ltd.  
The authors declare no conflict of interest.