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Deep Convolutional Neural Network for Condition Identification of Connections in Steel Structures



The deep learning technologies have transformed many research areas with accuracy levels that the traditional methods are not comparable with. Recently, they have received increasing attention in the structural health monitoring (SHM) domain. In this paper, we aim to develop a new deep learning algorithm for structural condition monitoring and to evaluate its performance in a challenging case, bolt loosening damage in a frame structure. First, the design of a one-Dimensional Convolutional Neural Network (1DCNN) is introduced. Second, a series of impact hammer tests are conducted on a steel frame in the laboratory under ten scenarios, with bolts loosened at different locations and quantities. For each scenario, ten repeated tests are performed to provide enough training data for the algorithm. Third, the algorithm is trained with different quantities of training data (from one to seven test data for each scenario), and then is tested with the rest test data. The results show that the proposed 1D-CNN with three convolutional layers provide reliable identification results (over 95% accuracy) with sufficient training data sets. It has the potential to transform the SHM practice.


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