# A Hybrid ANN-GA Model for an Automated Rapid Vulnerability Assessment of Existing RC Buildings
## 1 Introduction  
Significant loss of life and property due to structural damages and failures is frequently caused by earthquakes and similar natural disasters that strike different world regions. Especially, the large-scale damages after earthquakes reveal the importance of precautionary measures that can be taken before earthquakes [1; 2; 3; 4]. Designing earthquake-resistant structures and prioritizing existing vulnerable structures for further retrofitting plans are among the prominent measures that can extensively mitigate the following disasters since it does not seem possible to predict earthquakes with current technology [5; 6; 7]. Rocketing populations in urban areas and expanding building stocks day-by-day can pose severe risks of damage or failure depending on the characteristics of existing structures. In this context, determining the earthquake safety levels of buildings before a possible earthquake helps make accurate and fast decisions on the existing building stock [8; 9; 10; 11]. Many existing building stocks make it difficult to evaluate them in a reasonable timeframe and present challenges--like lack of qualified personnel and economic resources [12; 13]. Even a detailed examination of the seismic safety level of a building can take days. Therefore, it doesnot seem reasonable to examine every existing structure in detail. As a solution, risk priorities can be decided on a regional basis using methods that will provide faster and more accurate results than time-consuming techniques for individual structures. In this regard, rapid screening methods are used to determine the risk priorities of buildings. Therefore, the preliminary earthquake safety levels of the building stock in any region can be determined, and risk priorities can be decided on a regional basis [14; 15; 16]. The data obtained as a result of these methods can decide which buildings will be subjected to detailed structural analyses within the scope of performance-based earthquake engineering. This will significantly reduce the number of risky buildings subjected to detailed structural analyses. In order to overcome the destructive effects of earthquakes on the structural parameters and responses of buildings and reduce human losses, different countries have developed various approaches and methodologies related to rapid screening techniques. These methods enable decision-makers to adopt vital measures available in a modern pre-disaster management sense while also playing a pivotal role in spatial planning and urban transformation [17; 18; 19; 20]. These approaches were first put on a legal basis with the regulation published officially by the Ministry of Environment and Urbanization in 2013 in Turkey (PDRB-2013). With this regulation, the evaluation of risky structures and their regional prioritization are explained extensively for different structural systems. The performance scores for each building can be calculated based on the parameter values in this method. In this study, the performance scores were determined for 329 RC buildings to assess the earthquake risk priority using the Turkish rapid screening method by the authors within the scope of this study. Soft Computing (SC) techniques, including ANN, GA, and Machine Learning (ML) tools, have found many opportunities in different science and engineering applications. These methods can make more enhanced predictions while also being applied to solve intricate problems very fast with high levels of efficiency and performance. When it comes to structural and earthquake engineering applications, these techniques play a pivotal role in simulation, modelling, optimization, regression and classification, etc. [21; 22; 23; 24; 25]. ANNs have been developed based on the concept of imitating the behaviour of biological systems and the human brain in terms of learning, adaptation, and memory ability. General architectures that can be considered for a neural network are Feedforward (single-layer and multiple-layer), Feedback (Recurrent), and Mesh architectures [26].