CNN‐based damage detection of buildings from wave propagation between two adjacent floors

Abstract

Structural damages are expected to be detected from the changes in shear‐wave propagation in the vertical direction of the building. In the previous study, we constructed a convolutional neural network (CNN) model to recognize the changes in impulse responses at the inter‐floor with the virtual source at the top. However, it is difficult to tell the exact damaged stories, because it contains the information of all the stories between the virtual source and the receiver. In this study, to perform the story‐by‐story damage detection, wave propagation between two adjacent floors was proposed. The virtual source of the impulse response was changed from the top floor to the determined inter‐floor of the building. By recognizing the changes in the wavefield between the determined floor where the virtual source is located and its upper and lower adjacent floor, damages between two adjacent stories can be identified. The CNN model was used to automatically recognize the changes in the visualized impulse responses over time and validated using the data of a shake‐table test on a one‐third scaled 18‐story steel frame building.