# Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks
## 4 Case Study
### Benchmark Building and NLTHA
A total of 1993 worldwide horizontal historical GMRs with PGAs higher than 0.15 g (g = 9.81 m/s2) were selected from the PEER GMR database ([[https://ngawest2.berkeley.edu/](https://ngawest2.berkeley.edu/)]([https://ngawest2.berkeley.edu/](https://ngawest2.berkeley.edu/)), accessed on 5 April 2021) for the NLTHA of the benchmark building. However, due to the stringent seismic design of the building, only 7 out of the 1993 historical GMRs yielded a red tag, given the NLTHA results. A simple way to obtain sufficient strong GMRs was to uniformly scale up the historical GMRs by a scaling factor [2]. However, scaling factors, along with structural properties, GMR intensity, and types of seismic responses, can cause biased NLTHA results [2; 57; 58]. In the engineering seismology community, common scaling factors vary from 1 to 10 or more [58]. An alternative way is to synthesize GMRs based on historical GMRs even though it takes more computing time and resources. To  
Figure 4: (**a**) The fixed-base benchmark r/c frame with beam and column design information; (**b**) the fiber-distributed plasticity element of beams and columns with Concrete02 material model and Steel02 material model in OpenSEES; (**c**) the stress and strain relationship (**left**); and the hysteretic behavior (**right**) of Concrete02 (**top**) and Steel02 (**bottom**). obtain enough red tag GMRs and limit the bias introduced by scaling factors, the 1993 historical GMRs were scaled from 2 to 10 with an increment step of 1. Thus, 17,937 extra GMRs were obtained. Meanwhile, 2500 synthesized GMRs were generated, and they were spectrum- and energy-compatible with the historical GMRs, based on an algorithm developed in [59]. Therefore, 22,430 GMRs were obtained and inputted into the building model for NLTHA. To further limit the bias introduced by the potentially excessive scaling of historical GMRs, the MIDRs in the 22,430 GMRs were examined, and the GMRs resulting in MIDRs \(>\) 0.12 were excluded because the perimeter frame would have collapsed with an MIDR larger than 0.12, indicating it is impossible for the numerical model to have a converged solution due to instability. An examination of the excluded GMRs showed that most were scaled up with a factor over 8, which affirmed that the excessively scaled GMRs were removed. Among the remaining 17,647 GMRs, only 1067 GMRs were labeled with red tags. To form a balanced training dataset and to ensure that the performance of the neural network models was not biased by the class that had the most training GMR samples, 1067 GMRs were randomly selected from the green and yellow classes. Finally, a balanced dataset composed of 3201 GMRs was collected to train the neural network models where each green, yellow, and red class had 1067 GMRs, respectively. The process of GMR selection is shown in Figure 5.  
The 3201 collected GMRs were preprocessed, as mentioned in Section 3, to extract a duration of 30 s centered on their PGAs before training the three neural network models. Particularly, the extracted GMRs were encoded to 2D WT images for input into the 2D CNN model. However, the 1D GMRs were directly inputted into the 1D CNN model and the FNN model. The 3201 collected GMRs were randomly shuffled and proportionally divided into the training set, the validation set, and the test set by 0.70:0.15:0.15. An approximately balanced training set had 2241 GMRs made up of 754 green GMRs, 753 yellow GMRs, and 734 red GMRs. The validation set had 481 GMRs, and the test set had 479 GMRs. These three sets were used to train and test the three neural network models, as described in the following section.