###### Abstract  
This study introduces a multiple-input convolutional neural network (MI-CNN) model for the seismic damage assessment of regional buildings. First, ground motion sequences together with building attribute data are adopted as inputs of the proposed MI-CNN model. Second, the prediction accuracy of MI-CNN model is discussed comprehensively for different scenarios. The overall prediction accuracy is 79.7%, and the prediction accuracies for all scenarios are above 77%, indicating a good prediction performance of the proposed method. The computation efficiency of the proposed method is 340 times faster than that of the nonlinear multi-degree-of-freedom shear model using time history analysis. Third, a case study is conducted for reinforced concrete (RC) frame buildings in Shenzhen city, and two seismic scenarios (i.e., M6.5 and M7.5) are studied for the area. The simulation results of the area indicate a good agreement between the MI-CNN model and the benchmark model. The outcomes of this study are expected to provide a useful reference for timely emergency response and disaster relief after earthquakes. seismic damage assessment; multiple-input convolutional neural network; machine learning; nonlinear time history analysis; seismic response +
Footnote †: journal: Medical  
on nonlinear time history analysis [10; 11; 12]. The method can well consider the dynamic properties of buildings and time-/frequency-domain characteristics of ground motions. Nevertheless, the time history analysis-based method is computationally intensive and the simulation time can be relatively long for urban areas with a large number of buildings. Apart from time history analysis, fragility analysis has been widely adopted for the large-scale seismic damage assessment of buildings and infrastructures. For example, [PERSON] et al. [13] implemented fragility analysis to assess seismic safety of low-rise reinforced concrete school buildings; [PERSON] et al. [14] integrated fragility analysis with Bayesian updating for the seismic damage assessment of large-scale piping systems; [PERSON] et al. [15] conducted a seismic fragility assessment for masonry buildings in Portugal. In recent years, machine learning methods develop rapidly, which enables rapid assessment of regional building seismic damage. For example, [PERSON] et al. [16] proposed a seismic damage method for bridge structures based on decision tree, random forest, and XGBoost. [PERSON] et al. [17; 18] and [PERSON] et al. [19] proposed seismic damage methods based on a long short-term memory model (LSTM) and convolutional neural network (CNN), which can achieve a near real-time prediction of regional building seismic damages. [PERSON] et al. proposed a fuzzy-based model for structural damage assessment, which does not require special testing or long-term investigation [20]. Although there are many studies related to the seismic damage prediction of structures based on machine learning methods, the following two problems remain to be solved:  
(1) A lot of methods adopt ground motion intensity measures to reflect the destructive power of ground motions on buildings [21]. Nevertheless, when a structure enters the nonlinear stage, the correlation between the ground motion intensity measure and structural seismic response usually decreases significantly. For example, the correlation between \(Sa(T_{1})\) and the maximum top displacement of a five-story frame structure subjected to 201 different ground motions is demonstrated in Figure 1.  
The result of Figure 1 shows that \(Sa(T_{1})\) and the building maximum top displacement have a strong positive correlation (correlation coefficient \(r_{1}\) = 0.9184) when the ground motion intensity measure is small. Nevertheless, when the ground motion intensity measure increases and the building enters the nonlinear stage, the correlation between the ground motion intensity measure \(Sa(T_{1})\) and the maximum top displacement decreases significantly (\(r_{2}\) = 0.5110).