Abstract: In this paper, we use deep learning to investigate a loss factor function (LF) for measuring energy dispersal in bridge structures in Ho Chi Minh City, Vietnam. The LF is calculated from the power spectral density (PSD) of random vibration signals to account for the mechanical parameters required for detecting structural changes. The LF is applied to many different types of bridge decks such as a prestressed concrete bridge, precast reinforced concrete bridge, and cable-stayed bridge. In addition, to ensure the new parameters are working effectively for the evaluation, a deep learning-based signal processing platform is used along with a convolutional neural network (CNN) to create the training. The training process helps eliminate interference values and errors. This demonstrates that the LF is sensitive to many different real-life structures while previous parameters are sensitive to only particular structures.

Key words: Structural health monitoring (SHM); Convolutional neural network (CNN); Deep learning; Bridge monitoring; Viscoelastic model; Material properties; Loss factor

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