# A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring
## 1 Introduction  
Civil infrastructures such as buildings, roads and bridges will continuously accumulate damage due to material deterioration, natural hazard and harsh environments such as earthquakes, storms, fires, long-term fatigues and corroisons. Some of such anomalies could not be ignored since they may lead to catastrophic, economic loss, or even human life loss. Thus, the demand to monitor sustainable conditions of these infrastructures has increased dramatically, which is generally described as Structural Health Monitoring (SHM) [1] in the research community. Vibration-based methods have been widely used for damage detection and identification in SHM research [2]. The basic idea for vibration-based damage identification is that the structural physical properties (such as stiffness mass and damping matrices) will change once there are some damages occurred in the structure and these changes will lead the modal information (e.g., natural frequencies, mode shapes and other damage sensitive parameters) changes [3]. Therefore, by analysing these changes in the structural modal information of a given structure, one can detect the potential damagesand identify the location as well as the severity of damages. However, it is not practical to collect data under a big amount of different damage scenarios from real civil structures. A finite element model (FEM) of a given structure can be used to simulate different damage scenarios. The FEM updating technique has emerged in the 1990s as a subject of immense importance to the design, construction and maintenance of civil engineering structures [4]. It provides an efficient way to identify the structural damage and perform the assessment of the structure. This technique has been widely used in SHM applications for decades [5; 6]. [PERSON] et al. [7] have utilized the FEM updating method for assessing modal and structural parameters of a highway bridge. Also, a sensitivity-based FEM updating has been carried out for damage detection in [8]. In the past, one of the most popular non-linear approaches that have been widely adopted in SHM for vibrational damage identification is Artificial Neural Networks (ANNs). It was observed by [PERSON] et al. [9] that the joint damages occurring at the beam-to-column connections of a steel frame structure can be predicted from modal data via ANN approach. Also, a noise-injection learning method is proposed in which certain level of noise is injected into the training data, which can improve the robustness and accuracy of the damage detection algorithm [9]. A statistical approach is presented in [10] to consider the effect of uncertainties in developing an ANN model for structural damage detection. A back-propagation (BP) based neural network, which is one of the most popular algorithms for training ANNs, can perform well in estimating the location and severity of damages in a bridge structure [11]. It aims to find the minimum of the error function in the weight space by using the gradient descent optimization method [11]. Generally, the BP neural network works quite well in some particular circumstances [12; 13] when the initialized weights are quite close to a good solution, a large amount of training data is provided and enough computational resources are available. However, as the number of hidden layers increased, the 'gradient values' might be vanished during the process of backpropagation. Hence, it is difficult to optimize the weights in neural networks with a deep architecture. That is also a bottleneck for ANNs with deep architectures. The concept of Deep Learning Neural Networks (DLNN) was introduced by [PERSON] and [PERSON] [14] to reduce the dimensionality of data and overcome the above limitations. DLNN can learn the representative and discriminative features in a hierarchical manner from the data [15]. In contrast to the shallow linear and non-linear methods in ANNs, DLNN can achieve higher accuracy by using a larger number of layers.