# Prediction of Structural Type for City-Scale Seismic Damage Simulation Based on Machine Learning
## 1 Introduction  
Generally, cities are densely organized, with many buildings and civil infrastructures. If a city is affected by a strong earthquake, many casualties and significant losses will occur. For example, the 2011 Christchurch earthquake of New Zealand caused 185 deaths and a loss of US$ 11-15 billion [1]. Earthquakes pose a serious threat for many cities in China. For instance, Tangshan, a medium-sized city in China was hit by an Ms 7.8 intraplate earthquake on 28 July 1976, which caused more than 240,000 deaths, and razed the city of Tangshan [2]. Actually, two third of cities beyond one million people in China are located in high risk areas of earthquakes (i.e., the corresponding seismic precautionary intensities of these cities are more than 6 according to the seismic design code of China [3]). For example, Beijing, the capital of China, and Taiyuan, a large city in the north of China, are both located in the area of seismic precautionary intensity 8. Therefore, the earthquake safety of these cities deserves further study. A city-scale seismic damage simulation is important for earthquake disaster prevention and mitigation. Such a simulation can provide detailed results of potential building seismic damages, which can support decision making on urban planning for disaster prevention, seismic retrofit, earthquake emergency management, etc. Currently, the multi-degree-of-freedom (MDOF) model proposed by [PERSON] et al. [4; 5; 6; 7; 8] has been validated by actual earthquakes and successfully applied in several cities [5; 8; 9]. For instance, it is employed by the SimCenter project of the National Science Foundation of the United States to predict the seismic damage of 1.8 million buildings in the San Francisco Bay Area [10]. The MDOF model requires five parameters: Story area, story height, story number, construction year, and structural type. With the development of geographic information system (GIS) technology, many algorithms have been proposed to obtain the story areas and building heights automatically. For example, [PERSON] et al. proposed an algorithm for automatically detecting building footprints from very high resolution (VHR) satellite images by using a visual attention method and morphological building indices [11]. The story areas can be calculated by the polygons of building footprints. [PERSON] and [PERSON] proposed a shadow-overlapping algorithm for estimating building heights from VHR satellite images [12], by using graph theory and morphological fuzzy processing techniques. Note that imagery between 0.3-0.8 m/pxl is considered VRH satellite imagery in the above research. Story numbers can be calculated according to building heights and the design story heights of different building types. For example, the popular design story height is 3.0 m for residential buildings in China, according to the corresponding design code [13]. Additionally, the construction year can be quickly determined by the comparison of historical maps [14]. Currently, many cities have provided their building data for the public, e.g., the building data (e.g., footprints and construction years) of San Francisco can be downloaded by the DataSF website [15]. However, the data of structural types (e.g., masonry, frame, and shear wall structures) of the entire city are difficult to obtain, which limits the applications of city-scale seismic damage simulations based on the MDOF model. Specifically, the structural type of a building can hardly be identified by satellite image or maps; therefore, the structural type must be determined from the building's interior or by consulting relevant engineering drawings, which results in extensive manual workloads. Therefore, an efficient method to predict the structural types of building groups is necessary for a city-scale seismic damage simulation. Machine learning (ML) [16] can be employed to predict the structural building types of a city. Using ML, some potential patterns can be obtained to perform predictions from large amounts of data.