## 3 Near-Real Time Post-Seismic Evaluation of Structure Damage  
The first estimation of damage and loss of large building groups in near real-time after strong seismic events which strike big metropolitan areas constitutes an absolutely significant tool for authorities (i.e., the civil protection agencies) in the framework of the optimum prioritization of the essential post-seismic actions. Several methodologies were thus developed and proposed in order to contribute to this discussion. The available time rather than the accuracy of the required calculations for seismic vulnerability assessment is the crucial parameter under the post-seismic event circumstances. For this reason, the developed traditional methods proposed for these cases are based on Rapid Visual Screening (RVS) procedures. Although the strong motion parameters in near-real time after the seismic event are usually available due to the existence of accelerometric networks which can send the data by (i.e., the accelerograms) instantly exploiting the properties of the modern broadband internet networks, the corresponding instant collection of the data concerning the SDS of buildings in large scale is impossible. The current RVS procedures minimize the required time for the evaluation of the SDS of buildings because they are based on the macroscopic observation of the buildings' state after the seismic event. However, the performance of these procedures requires in situ surveys of experts. This is impossible to achieve in a large scale right after the seismic event. The ML-based algorithms, in combination with data for the SDS of buildings generated using numerical methods or collected from in situ surveys after previous strong events, can lead to the development of methods for the near real-time evaluation of the losses in city scale after an earthquake. The inherent ability of ANNs to incorporate the knowledge gained from previous procedures (through re-training), as well as their ability for the instant extraction of results, are the two main characteristics which give the potential for a rapid estimation of the SDS of numerous of buildings in stricken areas. This fact--which was investigated in several previous studies--is reflected in the research papers of [PERSON] et al. [3] and [PERSON] et al. [4]. [PERSON] et al. [3] utilized and evaluated several ML algorithms (Support Vector Machines, SVM; K-Nearest Neighbor, KNN; Bagging; Extra Tree, ET) in order to improve the efficiency of the RVS methods. The basic aim of their study is thus the investigation of the development of RVS procedures that exploit the benefits which arise from the simultaneous usage of ML techniques in the future. The authors used datasets generated by means of data collections from four earthquakes (Ecuador, Haiti, Nepal, and South Korea). The studied problem (with regard to RC buildings) was formulated and solved as a multi-classification problem. As regards the main conclusion extracted by the extended parametric analyses of the study, it was shown that the ET algorithm performed sufficiently with the four data-sets used. In general terms, the authors confirmed the increment of the accuracy of the RVS method when the ML algorithms are involved in the procedure. [PERSON] et al. [4] studied three different types of neural network models (1D Convolutional Neural Networks, 1D-CNN; 2D Convolutional Neural Networks, 2D-CNN; and Feedforward Neural Network, FNN) as regards their ability to effectively predict the SDS of RC buildings in near-real time. This evaluation was based on a benchmark RC frame building, while 1993 historical ground excitations properly selected from the PEER Ground Motion Records (GMR) database were used. From these historical GMRs, 3201 different GMRs were formed after processing for the generation of the training dataset.