## 1 Introduction
[15] proposed an ANN and genetic algorithm optimization to predict the different EDPs of low-rise buildings. [PERSON] et al. [16] presented a fragility analysis framework using a data-driven approach, and ANNs were used to conduct the seismic resilience assessment of bridge networks under spatially correlated earthquakes. [PERSON] et al. [13] found ANNs to be more suitable in estimating the probability of failure of nuclear power plant equipment. [PERSON] et al. [17] proposed the classical ANN model to develop non-parametric fragility curves to assess the earthquake risks of gravity dams. [PERSON] et al. [18] recently presented an approach coupling the genetic algorithm method and ANNs to predict the seismic responses of arch dams. They found that the proposed method could significantly increase the computational efficiency and replace some dynamic analysis calculations. [PERSON] et al. [19] provided a surrogate model that aimed to forecast the seismic responses of specific structural parts inside buildings characterized by intrinsic vertical and horizontal irregularities, leading to components with varying seismic vulnerabilities. [PERSON] and [PERSON] [20] presented a machine learning clustering approach to group buildings of the same type and choose key buildings to study how they respond to and are damaged by earthquakes in the area. [PERSON] et al. [21] used the ANN method to assess the impact of different materials on seismic vulnerability. [PERSON] et al. [22] implemented the Long Short-Term Memory model for Geospatial Information Systems to assess the earthquake vulnerability for the whole of India. [PERSON] et al. [23] developed a risk assessment machine learning tool for the purpose of retrofitting and to obtain potential design strategies for RC buildings. [PERSON] [24] used the ANN method for predictive PSDMs using a reputable ML approach. [PERSON] and [PERSON] [25; 26] developed the fragility parameters via a failure analysis for each damage state, using a Fault Tree Analysis and approximation of the fragility parameters in accordance with the rate of exceedance. [PERSON] et al. [27] proposed an artificial neural network framework to simultaneously predict the nonlinear seismic responses of all buildings in a cluster subjected to multi-earthquake inputs. More recently, DL algorithms for underground structures have been mainly used for underground rock engineering design [28], underground mine planning and scheduling [29], the forecasting of rock mass and concrete lining deformation [30; 31], and microseismic event recognition induced by underground excavatio [32]. There are relatively limited studies on the application of DL algorithms to predict the inelastic dynamic responses of underground structures [14]. Further studies are thus needed to utilize DL to help engineers and decision-makers in taking suitable intervention actions to prevent the possible repercussions of CFHT failure. To appropriately address the above difficulties, this study intends to use ANNs to develop high-fidelity and high-efficiency prediction models that can replace the extensive numerical simulation in performing seismic fragility assessments of CFHTs. This manuscript is organized as follows. The methodology of the ANNs and quality measures is briefly explained in Section 2. The seismic vulnerability analysis methodology based on maximum likelihood estimation (MLE) and IDA is presented in Section 3. Then, the FE numerical model of CFHTs considering a multimedium-coupled interaction system is presented in Section 4 to estimate the structural responses. Afterward, the ANN models with different hyper-parameters are discussed in Section 5. The analysis results and discussion are presented in Section 6. Finally, the concluding remarks derived from the results are presented in Section 7.