## 1 Introduction
Further field validation was conducted in a steel stringer bridge with area sensing [17]. For civil and industrial structures, it is difficult to monitor the structural health state in real time when the types of structures are complicated and the signals measured from the structures are corrupted due to environmental noise. Therefore, it is necessary to propose a non-model based approach to identify structural damage. Deep learning can address this problem in a satisfactory way due to its superior adaptive learning of datasets. A large and deep convolutional neural network (CNN) was employed to make a classification of 1.2 million high-resolution images [18]. Derivation and implementation of convolutional neural networks were presented [19]. A vibration-based damage detection and localization in real-time was proposed by using 1D convolutional neural networks [20; 21; 22; 23; 24]. A sparse coding algorithm was applied to a large number of unlabeled examples to train a feature extractor, and features were then used to be fed to a neural network classifier to distinguish various damage statuses of bridges [25]. To overcome challenges such as lighting and shadow changes, a type of convolutional neural network with deep architecture was used for detection of concrete cracks without directly extracting defect features [26]. Responses of a healthy and loosened connection structure were analyzed via wavelet coefficient differences to indicate damage of a steel structure [27; 28]. Wavelets were used to detect spikes in the wavelet details of responses of a framed structure subjected to strong earthquake excitation, indicating damage occurrence [29]. A combined wavelets, neural networks, and Hilbert transform, inspired by the deep learning paradigm, was presented to form a new signal-processing algorithm [30]. This was further studied to identify the fault condition of a roller bearing using three types of deep neural network models such as deep Boltzmann machines, deep belief networks and stacked auto-encoders [31]. Compared with a traditional support vector machine and back-propagation neural network, conditions of the planetary gearbox could be detected effectively with the best diagnosis accuracy using deep convolutional neural networks [32]. By applying wide linear models and deep neural networks, joint wide and deep learning was employed to combine the benefits of both generalization and memorization capability for recommender systems [33]. Concrete slabs with induced damage were investigated by applying big data analytics in online structural health monitoring [34]. A multi-scale structural health monitoring system was constructed to monitor the health states and assess the serviceability of large-scale bridges by employing the Hadoop Ecosystem (MS-SHM-Hadoop). A Bayesian network was studied to evaluate the reliability of specific components according to serviceability and inter-component correlations [35] and a hybrid response surface method [36]. Unlike buildings or bridges, for pipeline systems damage usually initiates from the internal surface especially when the internal temperature is higher than external temperature. Micro damage, matrix cracking [37], delamination [38; 39], Webepage [40], and fiber failure are the three stages that result in ultimate failure of FRP composite structure, and damages may probably initiate and concentrate inside the pipes rather than outside the surface. Previous studies have shown that by acquiring images from structural damage areas (mostly outside surfaces of structures), it may be good to distinguish the different health states of structures. Images captured from the external surface of pipelines may not be an appropriate way to identify damage. While modal macro strain (MMS) is an effective way to reflect damage condition, it has been only demonstrated in a 1D beam model [13; 14; 15; 16], and a 3D pipeline model has not been validated.