# Special Issue on Advanced Technologies in Seismic Design, Assessment and Retrofitting
## 2 A Brief Overview of the Published Papers
### Machine Learning Algorithms for the Prediction of the Seismic Response of Structures  
Machine learning algorithms are increasingly used to investigate methods to assess the seismic vulnerability of existing structures (Figure 2). The inherent ability of these algorithms to deal with multiparametric problems and to be continuously \"trained\" with new data resulting from real seismic excitations makes them powerful computational tools.  
[PERSON] and [PERSON] [21] developed the MLPER (machine learning-based prediction of structures' earthquake responses) model. The MLPER model was applied to predict the acceleration time history of the top floor translational degree of freedom of an n-story building without performing finite element analysis. The architecture of MLPER is based on the operation of a trained convolutional neural network (CNN) and is realized in three basic stages (encoding, latent space and decoding). The operation of CNNs requires the input of data in image form. In the case of MLPER, the input \"parameters\" are 2D images of the time history of the ambient responses of buildings in combination with the target seismic event. It is important to note that the input time histories are transformed from the time domain to the frequency domain in order to address a number of problems, such as the problems arising from the exceptionally large difference in the values of ambient response, those of earthquake excitation and those of the responses under earthquake. Thus, the input format of the CNNs is, in this case, two pairs of images, i.e., amplitude and phase spectrograms, corresponding to ambient responses and earthquake data, respectively. Similarly, the output is a pair of images consisting of amplitude and phase spectrograms. It should be noted, however, that the output images concern earthquake-induced responses. The time domain evaluation of the model (performed with various metrics, such as the mean absolute percentage error (MAPE) and the mean deviation angle (MDA)), showed very satisfactory results. The development of MLPER sets the basis for addressing the problem of the accurate and rapid prediction of the seismic response of buildings of various types considering different significant factors (i.e., the site effects, the second-order effects and the behaviour that differs from the bilinear stiffness).  
[PERSON] et al. [22] developed RASDA (rapid seismic damage assessment) software to support the rapid visual inspection (RVI) of existing reinforced concrete buildings. In this software, they incorporated trained artificial neural networks (ANNs) and, more specifically, multilayered feedforward perceptron neural networks (MFPNN). The seismic vulnerability assessment of existing buildings is generally divided into three levels, with increasing complexities in each of them. The first level is based on the RVI procedure carried out using specific templates without the need for complex calculations. The procedure followed is therefore simplified in order to allow the inspection of a large number of existing buildings in the shortest possible time. The proposed software has a built-in, user-friendly graphic user interface (GUI), with which it is possible to quickly input and process the required data collected during a RVI either pre-seismically or post-seismically. Moreover, with the built-in trained MFPNN, it has the capability--based on the data entered by the user and by solving a pattern recognition problem--to extract a prediction for the level of seismic damage of the studied buildings by classifying them in one of three damage categories/classes (null-slight, moderate and severe-collapse), as is typically carried out in the context of RVI after strong earthquakes. The evaluation of the proposed software was documented using an extended, numerically generated dataset of reinforced concrete buildings subjected to previous strong earthquakes. For this evaluation, confusion matrices (which are the main tools utilised to check the reliability of pattern recognition problems) were used.