# Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks
## 1 Introduction  
Rapid and accurate seismic damage assessment is essential to post-event response and rescue. Traditional approaches generate the fragility estimates of different structural damage states based on low-dimensional intensity measures (IMs, usually a scalar IM or two-element vectorial IM) of ground motion records. These approaches include cloud analysis [1], incremental dynamic analysis (IDA) [2], multiple strip analysis (MSA) [3], and improved models [4; 5; 6; 7; 8; 9]. However, according to previous studies, low-dimensional IMs were not sufficient to capture and propagate primary earthquake uncertainty through seismic fragility analysis [10; 11; 12; 13]. To overcome the disadvantage of low-dimensional IMs, multivariate regression models were used to obtain a fragility estimation and facilitate more accurate and reliable regional seismic risk estimates by incorporating multiple IMs (up to five) into a vectorial IM [14]. Two artificial neural network (ANN) models, i.e., the feedforward neural network (FNN) and radial basis function (RBF) network models with 14 IMs as inputs, were adopted to predict the seismic-induced damage states of 30 reinforced concrete (r/c) buildings [15; 16; 17]. It was found that trained FNN and RBF models could reliably and rapidly predict the resultant damage states of r/c buildings after earthquake events. Machine learning (ML) techniques were also used for the seismic-damage classification. [PERSON] et al. selected up to 48 IMs as inputs to train ML models to predict the damage states of structures [18]. Although these regressive and artificial intelligence models were able to consider high-dimensional IMs in seismic damage assessments, they required careful selection and computation of hand-crafted IMs from any IM candidate pool that consisted of dozens, if not hundreds, of developed IMs because what constituted an optimal IMto characterize ground motion records would change based on different structural types, collected ground motion records (GMRs), and structural damage indices [13; 19; 20; 21]. To better consider the uncertainties in seismic damage assessment and avoid the selection and the computation of hand-crafted IMs, GMRs were directly used as inputs for CNN-based seismic damage assessment and seismic vibration identification [22; 23; 24; 25]. Similar to the application of CNNs in 2D image and video analyses [26; 27; 28], the 2D GMR images were generated from raw 1D GMRs with wavelet transform (WT) encoding for CNN-based seismic assessments [22; 23; 24]. WTs have a long history of being applied in GMR classification. For example, in [29; 30; 31], WTs provided detailed time-frequency information of GMRs in the form of 2D images through time-variant spectral decomposition to classify the near-fault GMRs. However, there were other methods for encoding 1D time series to 2D images, such as recurrence plot (RP) [32], Gramian angular summation/difference fields (GAF), and Markov transition fields (MTF) [33]. The test of RP and GAF-MTF encoding techniques on the same dataset from the University of California's Riverside Time Series Classification Archive [34] indicated that the RP encoding obtained better prediction performance with a smaller encoding data size in the CNN-based time-series image classification than that of the GAF-MTF encoding. In the previous work of [PERSON] et al. [25], RP encoding and the newly proposed time-series segmentation (TS) were compared to the widely used WT encoding to explore the most suitable image encoding method for CNN-based seismic damage assessment. It was found that the WT encoding generated the highest prediction accuracy, as compared to RP and TS. Generally, WT encoding is the best technique for GMR image encoding for CNN-based seismic damage assessment in terms of prediction accuracy. Although the contemporary CNN-based seismic damage assessments avoid the computation and selection of hand-crafted IMs, the encoding of a 1D GMR time series to 2D GMR images increases the training time for a large set of GMRs.