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- W4386230003 abstract "The traveling velocity of a shear wave within a building structure will be slow down due to the degradation of story shear stiffness caused by non-linear response or damages (e.g., beam-end fractures). Hence, the shear-wave travel time can be used to evaluate the damaged states of buildings. In a previous study, we constructed a CNN model to recognize the changes of shear wave velocity at inter floors with the virtual source at the top. However, determining the exact damaged floor is difficult because it contains the information of all the stories between the virtual source and the receiver. Therefore, in this study, we changed the virtual source from the top to the determined inter floor to reconstruct the wavefield, enabling us to achieve the detection of story-by-story damage. By recognizing the changes of the wavefield between the determined floor where the virtual source is located and its upper and lower adjacent floor, damages between these two adjacent stories can be identified. To train and test the CNN model, we simulated the dataset using multiple-degree-of-freedom models under various ground motions. We verified the trained CNN model using acceleration records of a shake-table test of a 1/3 scaled 18-story steel frame building." @default.
- W4386230003 created "2023-08-29" @default.
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- W4386230003 date "2023-01-01" @default.
- W4386230003 modified "2023-09-30" @default.
- W4386230003 title "Story-by-Story Building Damage Recognition from Changes of Wave Propagation Between Two Adjacent Floors Using CNN" @default.
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- W4386230003 doi "https://doi.org/10.1007/978-3-031-39117-0_48" @default.
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