## 3 The Proposed Method for the Rapid Seismic Damage Assessment of RC Buildings  
In recent years, there has been a growing trend of using artificial intelligence techniques to create tools that can assist engineers, architects, and policymakers in making informed decisions about building safety. The aim is to develop tools that are accurate, scalable, and user-friendly, with the potential to enhance the effectiveness of current practices in these fields. The overall vision of using machine learning (ML) in rapid visual screening (RVS) for buildings is to leverage the power of data-driven models to improve the accuracy and \begin{table}
\begin{tabular}{c c} \hline \hline
**Damage State** & **Qualitative Description** \\ \hline _Slight damage_ & No permanent drift. Structure substantially retains original strength and stiffness. Minor cracking of facades, partitions, and ceilings as well as structural elements. All systems important to normal operation are functional. \\ \hline _Moderate damage_ & Wider cracks at non-structural elements, in-plane or out-of-plane. Some residual strength and stiffness are left in all stories. Gravity load-bearing elements function. No out-of-plane failure of walls or tipping of parapets. Some permanent drift. Damage to partitions. Cracking of facades, partitions, and ceilings as well as structural elements. Flexural and shear cracks at structural elements, concrete spalling. \\ \hline _Major damage_ & Local in-plane and out-of-plane failure of nonstructural walls, infills, etc. Wide flexural and shear cracks at structural elements, hoop fracture, buckling of longitudinal reinforcement, initiation of concrete core crushing. Little residual stiffness and strength, but loadbearing columns and walls function. Large permanent drifts. Some exits are blocked. Infills and unbroaced parapets failed or at incipient failure. \\ \hline _Collapse_ & Loss of load-carrying capacity, locally or globally. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification and description of seismic damage according to FEMA. efficiency of the screening process. By integrating ML techniques into RVS, it is possible to automate and standardize the screening process, reducing the reliance on human judgment and improving the consistency of the results. ML algorithms can be trained on large datasets of building characteristics and historical seismic data to identify patterns and correlations that can be used to predict the seismic performance of buildings. This can help prioritize buildings for more detailed assessment and retrofit, leading to a more targeted and efficient use of resources. The rapid seismic damage assessment method proposed is based on the formulation and solution of a pattern recognition (PR) problem. The PR is one of the problems that can be solved using machine learning (ML) algorithms [11; 12]. For this reason, artificial neural networks (ANN) and, more specifically, multilayered feedforward perceptron neural networks (MFPNN) are implemented. The theory and the applications of the MFPNN are described in detail in several books (e.g., [35; 36; 37]). The first step towards the development of a methodology for the damage assessment of RC buildings using PR and MFPNN is the definition of the relevant input and output parameters, considering the basic principles of the PR problems and the solution procedure using MFPNN. By definition, PR is the procedure for detecting and classifying objects of the same type into specific classes. A PR problem can be defined using three different approaches: the supervised learning approach, the unsupervised learning approach, or the reinforcement learning approach [37]. The software application developed herein applies the _supervised learning approach_. Therefore, the target of the applied algorithm is the consistent classification of objects into pre-defined classes. For this reason, the creation of an appropriate dataset and the training of a properly configured MFPNN using this dataset are required.