## 1 Introduction  
Bridges are prone to deterioration caused by external loads, such as traffic and environmental loading or natural disasters; therefore, structural-health monitoring (SHM) is critical to ensure safe and reliable operation of the bridges during their service life. Numerous vibration-based damage detection techniques have been studied in an attempt to monitor structural health [1, 2, 3, 4, 5, 6, 7, 8, 9], and they can be classified into two groups: (1) Parametric model-based methods that utilize the finite-element (FE) model of a structure and update the model parameters using acquired sensor data and (2) data-driven vibration-based damage identification techniques that use a database of measurements to fit a statistical model by extracting features (e.g., natural frequencies, mode shapes, modal flexibility, and curvature), and analyzing the condition of a structure. The parametric-model-based method can optimally calculate structural properties, such as modulus of elasticity and moment of inertia, but the precision of these properties heavily depends on the accuracy of the initial FE model and optimization methods. Data-driven methods have received greater attention because they are simple to implement. However, features from measurements,such as mode shapes and natural frequencies, may not properly be extracted because of measurement noise and the poor excitation quality of a structure, resulting in inaccurate damage detection. Recently, deep-learning-based damage detection (DLDD) has emerged as an alternative [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26] to conventional data-driven approaches. The DLDD automatically learns complex features from raw measurements other than dynamic characteristics and conducts nonparametric nonlinear regression for damage detection. [PERSON] et al. [10] proposed a time series one-dimensional convolutional neural network (1-D CNN) to extract features directly from only 20 s of raw acceleration without any hand-crafted feature extraction process. The training datasets were simulated with a FE beam model subjected to random excitation. The research used two different types of loss function to locate damage and estimate the quantity of the damage. [PERSON] et al. [11] used a deep network structure that was similar to that of [PERSON] et al. [10] using a 1-D CNN for classifying the states of a bridge. Their research used chunks of every 0.6 s of time-series measurement as an input, and they experimentally validated the performance of their trained network. [PERSON] et al. [12] proposed an autoencoder-based deep neural network for detecting damage of a tendon in a prestressed girder using 20 s of raw measurement numerically. Utilizing 2-D convolution, [PERSON] et al. [13] converted time-series measurement into an image and used a 2-D CNN for damage state identification. Although the results from the abovementioned research have shown the possibility of using CNNs for automated damage detection, ambient vibration testing requires sufficient measurement time until major vibration modes can be clearly identified. Therefore, increasing the length of inputs for the deep-learning network is critical for accurate structural damage detection. To address this issue, [PERSON] et al. [14] and [PERSON] et al. [15] used mode shapes and natural frequencies. Because modal parameters are fixed with the number of sensors, these parameters are extracted from ambient vibration testing and can be used without adjusting the size of the input. However, owing to the sparsity of the mode shapes, the accuracy of these methods depends entirely on the number of sensors instrumented on a structure. Table 1 summarizes the type of input and deep-learning model used for each method related to automated damage detection. In this report, a time-series deep neural network is presented for automated structural damage detection using a data normalization technique and 1-D CNN.