Abstract
Due to the importance of preventing unexpected structural failures, vibration-based damage detection methods have been widely studied to monitor the health of various structures. These methods are considered as efficient and reliable non-destructive techniques for damage detection of structures. In this study, a novel model is developed to predict damage severity in beam-like structures based on the finite element method (FEM) and multilayer perceptron (MLP) neural network. In this way, empirical data are obtained using FEM, then MLP neural network is used to predict damage’s severity. The first five frequencies and severity of the damage of the beam are considered as inputs and outputs of the neural network, respectively. Based on the inputs and outputs, the network creates a nonlinear activation function and is trained to predict the severity of the damage. Results obtained indicate that the model created by the network can detect any single damage in beam-like structures with high accuracy (R > 0.9) by having natural frequencies of the damaged beam.
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Saadatmorad, M., Siavashi, M., Jafari-Talookolaei, RA., Pashaei, M.H., Khatir, S., Thanh, CL. (2021). Multilayer Perceptron Neural Network for Damage Identification Based on Dynamic Analysis. In: Bui, T.Q., Cuong, L.T., Khatir, S. (eds) Structural Health Monitoring and Engineering Structures. Lecture Notes in Civil Engineering, vol 148. Springer, Singapore. https://doi.org/10.1007/978-981-16-0945-9_3
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DOI: https://doi.org/10.1007/978-981-16-0945-9_3
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