## 7 Final Remarks  
The work presented herein explored the application of a deep learning methodology to predict the EDPs and generate fragility curves. For this purpose, the regression algorithms of artificial neural networks (ANNs) were investigated by tests to identify the optimal hyper-parameters to predict the structural response. The main seismology characteristics were also considered in different cases, and multivariate earthquake IM features can be integrated into one seismic IM feature through dimensionality reduction. Then, a three-dimensional (3D) numerical cross-fault hydraulic tunnel (CFHT) considering the nonlinear interaction system was chosen as a typical structure to conduct the dynamic time history analysis. Given the input training database regarding the integrated IM and the structural response of the finite element model, the proposed ANN method helps to produce different prediction models. Subsequently, the statistical parameters of the structural responses in the prediction models were compared to test the ANN methodology, while the ANN-based fragility curves of IDA were constructed via the maximum likelihood estimation (MLE) method. From the attained results, the following conclusions can be obtained. 1. Compared with the hyper-parameters in terms of the mean square error, root mean square error, mean absolute error, and correlation based on the training database and testing database, it was found that two hidden layers and ten neurons are most suitable parameters for the ANN model. Meanwhile, it was demonstrated that the Bayesian optimization algorithm has higher predictive power for structural responses than other examined optimization algorithms. Furthermore, the direct use of the ANN model is not recommended to predict the structural response for the CFHT since different hyper-parameters have various abilities to change the predicted capacity of the model. 2. The predicted database of the integrated peak-value-type IMs of the input layers is more reliable than the scalar-valued IMs according to the examined methods. For the studied case, it was also confirmed that the amplitude type of seismology characteristics predicted by the ANN model has the highest prediction capacity compared with the other examined seismology features. On the other hand, the integral type of seismology characteristics is not suitable for the input layers of the ANN model to predict the structural response. 3. Only a slight difference was observed from the PSDM between the predicted EDPs and the calculated value of the CFHT, indicating that it is a reliable tool for the estimation of the seismic performance of CFHTs. Meanwhile, the ANN model is most suitable for the fragility analysis of the CFHTs because it can improve the computational efficiency and overcome the normally distributed assumption. For the seismic fragility curves, the traditional fragility analysis method may underestimate the probability of damage to CFHTs compared with MLE-IDA. In regard to this study, some limitations should be noted. For instance, the nonlinear dynamic plastic model was assumed to be the Drucker and Prager model, which may lead to dangerously biased conclusions for the tunnel under dynamic conditions. In addition, only the incoming angles of the vertical SV waves were utilized to describe the input of the earthquake load. Moreover, there is still a great deal of work that needs to be done in order to improve the proposed strategy. Different seismology features of earthquake records and multiple evaluation indicators of the structural response should be employed in order to more efficiently express the probability of damage of structures. The corrosion mechanism of the concrete lining induced by complex engineering environments should be further discussed in future studies. Meanwhile, the use of different metamodels, power or nonlinear regression models, statistical approaches, and probabilistic seismic hazard analysis can be considered to improve the prediction accuracy of the deep learning model.