## 1 Introduction
It should be outlined that despite the inherent simplifications and assumptions, the RVI methods are valuable since they can be applied in a short time after an earthquake event, providing a first-stage evaluation of the damage extent and highlighting the need for detailed assessment and retrofit measures. The reliability of the assessment results is increased when RVI methods are used in conjunction with results of non-destructive tests and simplified calculations, while the highest level of reliability is achieved when the assessment is based on analysis of the detailed structural model highlighting the retrofit needs, also providing an optimum retrofit scheme and prioritization of interventions. In line with the above, it is challenging to develop a framework, and a reliable tool for RVS of structures that could overcome the inherent disadvantages (mainly related to expert judgment) will be based on easily (in situ) collected data and require low computational effort. To this end, the current paper proposes a novel framework and a software application that exploits modern techniques and machine learning (ML) algorithms and is capable of extracting, in real-time, an estimation of the seismic damage assessment of individuals or groups of RC buildings either in the pre- or post-seismic phase. More specifically, the developed software tool is based on the expression of the seismic damage assessment as a pattern recognition (PR) problem [11; 12]. This is a novel approach that differentiates the proposed software tool from other similar applications. By formulating the problem as a PR problem, the seismic damage assessment is achieved in a more supervisory manner and with less sensitivity to possible inaccurate data estimates, as the proposed methodology extracts the classification of buildings into damage categories directly and not through the quantitative estimation of a damage index. The first step of RVI methods, either in the pre- or post-seismic phase, is the classification of structures into classes accounting for their seismic damage extent. This approach is compatible with the scope of the PR problems, which are dedicated to the classification of objects of the same type into pre-defined classes. The basic idea for the framework and software developed is to relate the macroscopic structural parameters (collected in the framework of RVI) and the seismic input parameters (based on selected accelerograms) to the expected seismic damage extent that is qualitatively and quantitively defined through seismic damage classes (SDC). Therefore, a preliminary rating of a structure is possible, which can be a valuable tool for rapid assessment and retrofit prioritization for both preand post-earthquake assessment. Regarding the computational framework, the multilayered feedforward perceptron neural networks (MFPNN) were selected to solve the PR problem. Several research studies have highlighted the effectiveness of this type of artificial neural network (ANN) within the last three decades [13; 14; 15; 16; 17; 18; 19]. In addition, it should be noted that the use of ANNs as a computational tool in software development for the estimation of seismic damage assessment of structures has been proposed in recent years [20; 21]. The software developed and presented herein includes a user-friendly and powerful graphic user interface (GUI), enabling the quick and easy insertion of the required structural and seismic input data. The features available include the graphical representation of useful structural and seismic input data parameters and the calculation report of relevant indexes defining the RC buildings' seismic damage state (SDC), thus enabling rapid damage assessment. The development of the software using modern development platforms [22; 23] and the structure of the corresponding source code enables its wide implementation for pre- or post-earthquake seismic damage assessment of existing RC buildings.