# Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks
## 4 Case Study
### Model Training, Validation, and Test  
The three models were trained and tested with the same datasets under the same computing configuration. A workstation with a GPU GeForce RTX 2080 (8 G memory) was used to train the three neural network models. The TensorFlow 2.2.0 platform [60] was  
Figure 5: The diagram of selecting the balanced dataset of 3201 GMRs.  
adopted to build and train the models. During the training process, the training set was mainly used to tune learnable parameters. In each training iteration, a batch of 32 GMRs from the training set was fed into the model to update the parameters with the Adam optimizer. Each training epoch had 70 training iterations to feed all 2241 training GMRs into the model once. Fifty training epochs were conducted for each model. The validation set was utilized to monitor the performance of the model during the training process, but it was not used to update the learnable parameters. The model checkpoints at which the trained models achieved the smallest validation losses on the validation sets were saved for assignment using the unseen test set. Figure 6a,b depicts the training and validation histories of the three models. The 1D CNN model and the 2D CNN model had similar loss histories using the same training and validation sets. However, the FNN model, which had higher losses, was less accurate with lower performance than the CNN models. This result indicated that the high-level features extracted by the convolutional and max-pooling layers in CNNs could better characterize the GMRs than the two-hidden-layer feature space in the FNN model. The stars in Figure 6a,b denote the epochs where the three models achieved the lowest validation losses. The tuned weights at these epochs were saved for a further performance assessment using the same unseen test set, which was not involved in the training process.  
The performance assessment of the test set is shown in Figure 6. Figure 6c-e represents the confusion matrices of the three models on the same test set. The test set had 479 GMRs composed of 155 green GMRs, 166 yellow GMRs, and 158 red GMRs. The rows of the confusion matrices were predicted classes by the neural network models, and the columns were the true classes of the GMRs obtained from NLTHA. The cells denoted as \"recall\" in the last row indicated portions of correctly classified GMRs in a true class. The cells denoted as \"precision\" in the last column were the portion of the true GMRs in a predicted class. The cell in the bottom right corner denoted as \"accuracy\" was the portion of correctly classified GMRs in one test set. Higher recall, precision, and accuracy close to 1 on the test set indicated better model performance [61]. Figure 6c-e shows that the 1D and 2D CNN models had close recall, precision, and accuracy. However, the FNN model had lower recall, precision, and accuracy than the two CNN models. This comparison  
Figure 6: The training, validation, and test results of the three neural network models: (**a**) the accuracy histories on the training and validation sets; (**b**) the loss histories on the training and validation sets; (**c**) the test confusion matrix on the test set of 1D CNN model; (**d**) the test confusion matrix on the test set of 2D CNN model; and (**e**) the test confusion matrix on the test set of FNN model.  
indicated that the CNN models outperformed the FNN model in the GMR-dependent seismic damage assessment.