# Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods
## 1 Introduction  
Destructive earthquake is a tremendous threat to the operation and security of civil infrastructures. For example, the Wenchuan earthquake in 2008 caused the collapse of a large number of buildings, resulting in more than 65,000 deaths [1]. Moreover, the Northridge earthquake in 1994 damaged California's transportation infrastructures, which seriously jeopardizes the transportation system [2]. The rapid assessment of earthquake destructive power to structures can provide a scientific basis for decision-makers, to facilitate the post-earthquake rescue and relief work. The earthquake destructive power to structures is influenced by the frequency-/time-domain characteristics of ground motions and the nonlinear seismic performance of structures. In previous studies, the seismic fragility analysis [3] and capacity spectrum method [4] have been frequently used to assess the earthquake's destructive power to regional infrastructures [5, 6]. With the advancement ofhigh-performance computing technologies and the higher accuracy requirement for seismic damage assessment, the time-history analysis (THA)-based method is gradually applied to the seismic damage simulation of regional infrastructures [7; 8; 9; 10; 11]. The THA-based seismic damage simulation method can adequately consider the frequency-/time-domain characteristics of ground motions and the nonlinear seismic performance of structures, to reasonably reflect the destructive power of earthquake to structures. Furthermore, the THA-based seismic damage simulation method can be applied to different types of structural systems and reveal the whole process of failure mechanism of a structure [11; 12]. Therefore, the THA-based seismic damage simulation is adopted as the benchmark method in this study. To date, machine learning methods have been widely used in various fields, such as the recognition of visual objects, natural language, and music [13; 14; 15; 16]. Moreover, there are also many applications of machine learning methods in earthquake engineering [17; 18; 19; 20; 21; 22; 23; 24; 25]. [PERSON] et al. [17] used several machine learning methods to predict structural types based on regional building attribute data. [PERSON] et al. [18] suggested an artificial neural network-based multi-parameter fragility methodology that can generate bridge-specific fragility curves without grouping the bridge classes. [PERSON] et al. [19] proposed a hybrid particle-swarm-optimization-based artificial neural network model to calculate horizontal displacement of columns subjected to an earthquake. [PERSON] et al. [20] proposed a physical-guided convolutional neural network, which can make full use of the physical model and only adopt a small training dataset to realize an accurate simulation of structural responses. [PERSON] et al. [21] proposed a structural fragility curve generation method based on multiple machine learning methods. [PERSON] et al. [22] proposed an automated building seismic damage assessment method using an uncrewed aerial vehicle (UAV) and a convolutional neural network (CNN), which can obtain the damage distribution of an area with an accuracy of 89.39%. Moreover, extensive research works have been reported on the machine learning-based seismic response simulation (Table 1). [PERSON] et al. [23; 24] adopt various machine learning methods to predict the damage state of a structure. It is a classification problem, and the computational workload of the simulation is light. Nevertheless, in some cases, obtaining engineering demand parameters (EDPs) is more favorable for damage or loss assessment of a structure. In the study of [PERSON] et al. [25], long-short-term memory (LSTM) [26] network is used to predict the structural time-history response. The LSTM network requires a relatively large computational workload [27] and is not suitable for the response prediction of a large number of structures. This study focuses on the fast prediction of EDP under seismic excitation.