# Trident: A Deep Learning Framework for High-Resolution Bridge Vibration Monitoring
###### Abstract  
Bridges are the essential components in lifeline transportation systems, and their safe operation is of great importance. Information on structural damage could assist in timely repairs and reduce downtime. With the latest advancements in sensing technology, collecting vibration data from bridges has become more accessible. However, effective vibration processing is still a challenge, given the high dimensionality and massive size of vibration data. Existing studies have shown that machine/deep learning techniques can be valuable tools for this task. However, the learning and computational capacities of these models are challenged in the presence of large sensor arrays. We propose Trident as a novel deep learning framework that enables automatic damage feature extraction by simultaneously learning from temporal and three-dimensional (3D) spatial variations of 6D input data in instrumented bridges. Trident is equipped with 3 ConvLSTM3D branches to achieve this goal. A 3D steel truss bridge subject to dynamic traffic loads is monitored for its vibrations to evaluate Trident's robustness in finding damaged elements. A damage dataset of 52,800 vehicle passing simulations is generated leveraging a database of 528 passenger vehicles in the United States, obtained from the National Highway and Traffic Safety Administration. Bayesian optimization is utilized to tune the model's hyperparameters, achieving a test Node Average Geometric Mean Accuracy of 86%. This level of performance is promising given the high dimensionality and complexities of the output space in vibration-based monitoring. Trident's concept can be extended to other vibration monitoring tasks with different time series data and damage labeling strategies. deep learning; signal processing; structural health monitoring; traffic simulations +
Footnote †: journal: Medical  
global to local scales are examples of efforts in automating visual inspections [2; 3; 4; 5; 6]. A detailed review of deep learning applications in vision-based SHM can be found in [PERSON] and [PERSON] [7] and [PERSON] et al. [8]. There has been substantial progress in this domain mainly because neural network architectures initially proposed by deep learning research can benefit a variety of vision tasks, including images from building facades or highway bridge inspections. From a more technical point of view, the image dataset's input and output tensor structure does not change substantially despite the significant difference in data distributions. To this end, it is shown that transfer learning can also help to fine-tune pretrained models to perform well for structural engineering purposes [9]. Vibration-based health monitoring is an alternative to visual inspections that involves processing structural responses, like accelerations, into a measure of structural damage. Several works in the literature have utilized machine learning to process vibration data into information about the damage. Classical machine learning algorithms, including but not limited to K-nearest neighbors, tree-based methods, clustering techniques, multi-layer perceptrons, and support vector machines, have been studied for their robustness for damage diagnosis [10; 11; 12; 13; 14; 15; 16]. Most of these traditional ML models rely on highly processed input features, such as peak response or spectrum values for vibration records (e.g., due to seismic events). Obtaining these manually engineered input features is often associated with significant loss of information because time-series information is processed into highly compact vector representation with a few scalar indices. [PERSON] and [PERSON] [17] proposed the concept of Semantic Damage Segmentation (SDS) and a grid environment to process a large array of cumulative intensity features. The authors use a Fully Convolutional Neural Network (CNN) to predict the location and severity of damage in all the structural nodes of a 10-story-10-bay frame.