## 5 Conclusions  
An automated damage detection method using a deep neural network was presented. The proposed method for automated structural damage detection comprises two main contributions. (1) Data normalization is performed using NExT to compress and normalize the input data length. The acquired data can vary according to the measurement environment and purpose. Normalizing and quantifying the data length are critical to damage detection through deep neural networks because deep neural networks only work for trained data lengths. (2) A CNN is used to localize damaged elements. The proposed convolutional network can localize damaged elements from normalized input acceleration signals without any damage-sensitive extraction process. A numerical model of a simply supported beam was excited by random ambient load and traffic loading, and acceleration responses were extracted from nine nodes. Sensor noise is considered to demonstrate the reality of the measurement. Noisy acceleration signals from nine nodes were correlated with each other to normalize and quantify the data length, and a fully correlated response matrix was generated. The fully correlated response matrix is the input of the deep neural network, and the output is the location of the damaged element. For the training of the proposed method, datasets were generated using random excitation and traffic loading, and single damage was randomly applied to one of the elements. A total of 20,000 datasets for training (10,000 for each load) and 4000 datasets for validation (2000 for each load) were used to train the model. The number of intact data (i.e., category 0) was greater than each damage data (from category 1 to category 10) to balance the number of intact to total number of damage data. For representation of effect of data normalization, two training cases were compared. The resulting classification accuracy for Case 1 was 99.90% and 99.20% for random excitation and traffic loading datasets, respectively. Through the results, it was found that by using data normalization technique in the pre-processing step, longer data can be used for training to enhance classification capability of trained model. The proposed method was validated through tests with different conditions for generating datasets. For random excitation, the sampling time was increased. For traffic loading, the velocity, number of trucks, and sampling time were increased. In addition, 0% or 20-50% of damage was chosen at random for both loads. The classification accuracy of random excitation was 100% and that of traffic loading was 99.00%. Future work is planned to focus on problems caused by the low severity of damage. The proposed method using different deep neural networks rather than 1-D CNN will be studied to improve classification accuracy. Furthermore, to enhance the effectiveness of the proposed method, the classification of multiple damages and prediction of damage severity will be studied. Figure 7: t-distributed stochastic neighbor embedding (t-SNE) visualization of untrained new datasets with measurement size (e.g., 120–150 s) and damage severity (intact and random damage quantity of 20–50%): (a) Random excitation and (b) traffic loading. **Author Contributions:** All authors contributed to the main idea of this paper. [PERSON] and [PERSON] wrote the software and designed the numerical simulation. [PERSON] and [PERSON] analyzed the numerical simulation results. [PERSON], [PERSON], and [PERSON] wrote the article, and [PERSON] and [PERSON] supervised the overall research effort. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Smart Construction Technology Research Program funded by the Ministry of Land, Infrastructure and Transport of Korean Government (20 SMIP-B156007-01). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.