# Investigation of Structural Seismic Vulnerability Using Machine Learning on Rapid Visual Screening
## 1 Introduction  
The destructive potential of an earthquake constitutes one of the most severe and impactful engineering challenges faced by modern societies. On the one hand, the global population has increased dramatically during the last century. Coupled with a corresponding increase in urbanization, this has led to a concentration of humans and valuable infrastructure in large urban areas. On the other hand, many structures in the available building stock do not adhere to modern safety standards and, thus, seismic events over the past decades (USA, Turkey, Pakistan, India, Greece, New Zealand) have demonstrated the potential catastrophic results these earthquakes might cause. Government bodies should be able to identify the most seismically vulnerable buildings in the population. This should enable the allocation of the available resources for preventative actions. In this context, seismic vulnerability refers to the potential of a structure to suffer a certain degree of damage under an earthquake of a predefined intensity level [1, 2]. However, the seismic vulnerability of all buildings in a given population cannot be ascertained using the highest possible scrutiny (e.g., using analytical methods like step-by-step dynamic analysis) due to the limitation in resources. To this end, most countries have developed multi-step processes, wherein an initial screening step is performed to identify the potentially most vulnerable structures, which are subjected to further analysis later. The initial step in the aforementioned processes usually consists of a so-called Rapid Visual Screening (RVS) procedure where structural characteristics that are critical to the overall seismic behavior of the building are rapidly documented by an expert. Such characteristics could include the structural type, the underlying soil, or the height of the building. These are then weighted by expert-defined coefficients to produce a vulnerability index which can be used to rank the given population of structures. The subsequent analysis focuses on the top structures that were obtained according to the respective resource constraints. The first RVS procedure was developed by the USA Federal Emergency Management Agency (FEMA) in 1988 [3], which was subsequently revised and updated in 2002 [4]. Since the initial version of FEMA 1988, many countries and organizations that face similar challenges have developed their own adaptations of RVS. The most notable ones include the New Zealand Society for Earthquake Engineering [5], the Indian Institute of Technology [6], the Greek Earthquake Planning and Protection Organization (OASP) [7], and the Canadian Office of Critical Infrastructure Protection and Emergency Preparedness (OCIPEP) [8]. Inspired by FEMA, these adaptations share some common elements; however, as their variety indicates, they also have some differences with each other. Indeed, different countries and organizations consider different structural characteristics to be the most crucial and, thus, employ different sets of features in their respective RVS procedures. Even when the same sets of features are employed, different weights can be assigned to them according to the respective experts' opinions. The accuracy and efficiency of these methodologies can be assessed using data from near-field earthquakes [9; 10] which have also been used to calibrate the above procedures [11; 12]. On the other hand, the emergence and rapid development of Machine Learning (ML) techniques has highlighted the potential applicability of ML-based methodologies to tackle many complex challenges. To this end, researchers have proposed different approaches that combine the demonstrated power of ML with the established RVS procedures. [PERSON] et al. [13] trained a Convolutional Neural Network (CNN) to ascertain the presence of a soft story, given street view images. Similarly, [PERSON] et al.