## 1 Introduction  
Deep neural networks (DNNs) have gained significant popularity in addressing regression problems, and numerous architectural designs have become prevalent. One such architecture is the multi-layer perceptron (MLP), which has been widely employed in various regression problems [1]. The developers of the MLP are acknowledged for introducing the backpropagation algorithm, a key method for training neural networks [2]. Another notable architecture is the convolutional neural network (CNN) introduced by [PERSON] et al. [3, 4, 5, 6]. CNNs are extensively utilized in image and signal processing tasks and have also found application in regression tasks. Recurrent neural networks (RNNs), initially introduced by [PERSON] in the early 1980s [7], gained widespread adoption with the advent of the long short-term memory (LSTM) architecture developed by [PERSON] and [PERSON] in 1997 [8]. LSTMs have proven highly effective in capturing long-term dependencies in sequential data. During the mid-2000s, Restricted Boltzmann Machines(RBMs) and deep belief networks (DBNs) played a pivotal role in the advancement of deep learning techniques, particularly in unsupervised learning and feature learning [9]. Between 2000 and 2017, advancements such as dropout, batch normalization, convolutional LSTMs, and residual connections further enhanced the performance of existing architectures for regression tasks. Transformers, a more recent architectural design, have exhibited remarkable success in natural language processing tasks and exhibit potential for regression tasks [10]. Furthermore, it is crucial to emphasize the effectiveness of DNNs in addressing two particularly challenging concepts. The first concept involves using available photographs to assess seismic vulnerability, which can be appropriately processed to provide input data for empirical vulnerability algorithms [11]. The second concept revolves around the automated detection of defects in existing reinforced concrete (RC) bridges, necessitating the application of diverse deep learning (DL) methodologies and techniques to interpret the resulting predictions [12]. Meanwhile, seismic response prediction is an important aspect of earthquake engineering, and simplified models such as the SDOF and MDOF systems can provide valuable insights into the behavior of structures, being advantageous due to their processing efficiency, which allows for faster and more manageable computational simulations. However, these simplified models have limitations, particularly in accurately capturing the nonlinear behavior of structures and the spatial variability of ground motions, which is something we shall bear in mind. Software tools and methodologies such as Hazus-MH 2.1 in the United States [12], pre-quake rapid visual inspection (RVI) in Greece [13], and the FEMA P-58 methodology [14] have been developed to enable fast-track inspections and risk estimations for large building stock. Open-source software frameworks such as OpenSees, version 3.5.0 [15] and OpenQuake version 3.17.1 [16] have also been developed, which provide a platform for researchers and engineers to develop and apply advanced techniques for seismic response prediction, including machine learning and hybrid simulation. For example, OpenQuake has been used for probabilistic seismic hazard assessments and loss estimations [17]. While these tools have their own limitations, ongoing research is focused on improving their accuracy and applicability through advanced techniques and open-source software. Additionally, advancements in big data and structural health monitoring have created new opportunities for seismic response prediction methods. Structural health monitoring systems, such as accelerometers, provide real-time data on the behavior of structures during seismic events, enabling a detailed understanding of their response. This data, along with geological and seismic activity data, can be used to build large datasets for machine learning and other advanced techniques [18; 19].