## 7 Conclusions  
In the current study, the capability of artificial intelligence (AI) techniques in the load-slip behavior prediction of channel shear connectors was examined. Specimens of channel connectors with different dimensions were embedded in normal strength concrete and high-strength concrete (HSC). Several push-out tests were carried out, and the load-slip diagrams of the specimens were determined. In addition, the modes of failure in the specimens were reported. In order to eliminate the need for conducting the costly and time-consuming push-out tests, a hybrid artificial neural network-particle swarm optimization (ANN-PSO) model was developed. Besides, to validate and examine the performance of the ANN-PSO model, an artificial neural network (ANN) was also created and tuned by a commonly used backpropagation (BP) algorithm. Finally, the load-slip behavior of the tested channel connectors was predicted by both of the ANN-PSO and ANN-BP models. The following conclusions can be drawn from the paper:  
* In all the tested specimens, the mode of channel fracture was only seen. Therefore, it can be concluded that channel connectors are more likely to have this mode of failure in HSC. In addition, reinforcement could be mentioned as another reason for occurring this mode of failure, as the normal strength specimens also experienced the same failure even though they had lower compressive strengths.
* At the peak of load, channel connectors embedded in HSC showed a higher range of slip in comparison with that of the normal strength specimens. This shows that the type of concrete and compressive strength can change the behavior and ductility of channel connectors.
* Although the compressive strength of concrete was effective in the shear capacity of channel connectors and the specimens with higher compressive strengths resulted in higher shear capacities,  
Figure 11: Load prediction in the testing phase: (**a**) ANN-BP model; (**b**) ANN-PSO model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Load**} \\ \hline \multirow{2}{*}{**Performance Indices**} & \multicolumn{3}{c}{**Training**} & \multicolumn{3}{c}{**Testing**} \\ \cline{2-7}  & \(r\) & \(R^{2}\) & \(RMSE\) (kN) & \(r\) & \(R^{2}\) & \(RMSE\) (kN) \\ \hline ANN-PSO & 0.972 & 0.944 & 13.342 & 0.970 & 0.940 & 13.342 \\ ANN-BP & 0.955 & 0.912 & 16.747 & 0.950 & 0.902 & 17.109 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance indices of the ANN models for the load prediction.  
it seems that the length of channel connector is the most efficient parameter as the connectors with longer lengths could meet the higher shear demands.
* It was observed that developing an ANN model would be practical in the load-slip behavior prediction of channel connectors, thus eliminating the need for conducting costly experiments to some extent.
* Although both the ANN-BP and ANN-PSO models showed acceptable performance in the load-slip prediction, it has been shown that the PSO algorithm is capable of improving the accuracy of the prediction. Therefore, this algorithm can be practically used in the case of the load-slip behavior prediction of channel shear connectors.  
Conceptualization: M.S., M.S.M., and P.M.; investigation: A.B.; experiment: M.S. and H.N.; analysis of experimental results: M.N.A.S. and M.S.M.; data preparation: S.P.-N. and P.M.; methodology: P.M. and Y.Z.; models development: P.M., M.S.M., X.S., and J.D.; resources: H.N.; writing--Original draft preparation, A.B.; writing--Review and editing: M.S.M. and P.M.; project administration: M.S.; funding acquisition: H.N.  
This research received no external funding.  
The authors declare no conflict of interest.