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- W2017137095 abstract "This paper is primarily concerned with the application of static test data in conjunction with the probabilistic neural network (PNN) for the classification of damage patterns of a cable-stayed bridge. A total of 11 damage patterns are considered by combination of 5 typical damage regions. Both training and testing data, derived from static analysis via finite element method (FEM), are contaminated with different noise level to simulate the FE model and measurement errors. The study of damage pattern identification is conducted by taking into account the change ratios of the deflection of the main beam and the tower under loading as input neurons of the PNN. The effects of noise levels, the types of damage patterns, and the number of input neurons on the identification accuracy are investigated. Based on the classification results some valuable conclusions were obtained." @default.
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- W2017137095 date "2003-08-19" @default.
- W2017137095 modified "2023-09-23" @default.
- W2017137095 title "Application of probabilistic neural network and static test data to the classification of bridge damage patterns" @default.
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- W2017137095 doi "https://doi.org/10.1117/12.482684" @default.
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