Dynamic displacements-based model updating with motion capture system

Oh, Byung Kwan; Hwang, Jin Woo; Choi, Sanghee; Kim, Yousok; Cho, Tongjun; Park, Hyo Seon

doi:10.1002/stc.1904

Cited by 11 publications

(2 citation statements)

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“…When seismic performance evaluation is required in the event of a strong seismic event, the time histories of acceleration responses measured from the building during the seismic event are entered into the constructed CNN, and the time histories of displacements during the event are predicted. Therefore, for application of the proposed model, an SHM system for measuring acceleration and displacement in a building is required . Figure shows the required SHM system, for which the premises that displacement measurement may be impossible in the event of a strong EQ and that the acceleration measurement equipment can function even in the event of a strong EQ (because it behaves as a part of the building floors) are adopted.…”

Section: Seismic Response Prediction Methodsmentioning

confidence: 99%

“…Therefore, for application of the proposed model, an SHM system for measuring acceleration and displacement in a building is required. 28,29 Figure 1 shows the required SHM system, for which the premises that displacement measurement may be impossible in the event of a strong EQ and that the acceleration measurement equipment can function even in the event of a strong EQ (because it behaves as a part of the building floors) are adopted. Furthermore, the structural responses for multiple EQs must be recorded before the occurrence of a strong EQ that may damage the structure.…”

Section: Seismic Response Prediction Methodsmentioning

confidence: 99%

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Seismic response prediction method for building structures using convolutional neural network

Park

2020

Struct Control Health Monit

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Summary In this study, a method of predicting the seismic responses of building structures based on a convolutional neural network (CNN) is proposed. In the method, the time histories of acceleration responses previously measured in a building during earthquakes are used in the CNN input layer, with the corresponding time histories of the displacement responses being used in the CNN output layer. The correlations between the features automatically extracted from the acceleration responses by the convolution and pooling operations in the various CNN layers and the displacement responses in the CNN output layer are established through CNN training. The trained CNN predicts the displacement responses for a future earthquake event using only the measured acceleration responses. To verify the validity of the proposed method, a numerical study on the ASCE benchmark model and an experimental study on a reinforced concrete frame structure are conducted. In the numerical study, the structural responses of the ASCE model subject to artificial earthquakes are utilized for CNN training, and the performance of the trained CNN is verified through seismic response prediction. In the experimental study, shaking table tests are performed for various earthquakes to measure the seismic responses of the reinforced concrete test structure. The seismic response prediction performance of the proposed method is examined using CNN trained with the measured seismic responses. Furthermore, the performances of CNNs trained with different numbers of datasets generated by the proposed data generation method based on data overlapping with the same data pool are discussed with related to the number of CNN training iteration number.

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Section: Seismic Response Prediction Methodsmentioning

confidence: 99%

Section: Seismic Response Prediction Methodsmentioning

confidence: 99%