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A platform for research: civil engineering, architecture and urbanism
Structural Damage Detection Using Reduced Free Vibration Data and Deep Learning
This work presents a damage detection method for trusses using reduced free vibration data and multiple deep neural networks (DNNs). For this aim, a dataset randomly created by finite element analysis (FEA) is employed to build the DNN model. Inputs are a reduced free vibration dataset only including eigenvalues at several degrees of freedom (DOFs) of a few first modes, while outputs are damage ratios of truss members. Accordingly, the DNN requires a simpler architecture and less computational cost for the training and testing processes. By eliminating low-risk members via a damage threshold, the subsequently trained and tested DNN models become more accurate in predicting the location and severity of damaged members. A 2D truss programmed by Python is tested with two different damage scenarios to verify the reliability of the suggested approach.
Structural Damage Detection Using Reduced Free Vibration Data and Deep Learning
This work presents a damage detection method for trusses using reduced free vibration data and multiple deep neural networks (DNNs). For this aim, a dataset randomly created by finite element analysis (FEA) is employed to build the DNN model. Inputs are a reduced free vibration dataset only including eigenvalues at several degrees of freedom (DOFs) of a few first modes, while outputs are damage ratios of truss members. Accordingly, the DNN requires a simpler architecture and less computational cost for the training and testing processes. By eliminating low-risk members via a damage threshold, the subsequently trained and tested DNN models become more accurate in predicting the location and severity of damaged members. A 2D truss programmed by Python is tested with two different damage scenarios to verify the reliability of the suggested approach.
Structural Damage Detection Using Reduced Free Vibration Data and Deep Learning
Lecture Notes in Civil Engineering
Reddy, J. N. (editor) / Wang, Chien Ming (editor) / Luong, Van Hai (editor) / Le, Anh Tuan (editor) / Dang, Khanh D. (author) / Truong, Hoa H. (author) / Luong, Van Hai (author) / Le, Tuan A. (author) / Lieu, Qui X. (author)
The International Conference on Sustainable Civil Engineering and Architecture ; 2023 ; Da Nang City, Vietnam
Proceedings of the Third International Conference on Sustainable Civil Engineering and Architecture ; Chapter: 168 ; 1565-1571
2023-12-12
7 pages
Article/Chapter (Book)
Electronic Resource
English
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