This study presents a convolutional neural network (CNN)-based response estimation model for structural health monitoring (SHM) of tall buildings subject to wind loads. In this model, the wind-induced responses are estimated by CNN trained with previously measured sensor signals; this enables the SHM system to operate stably even when a sensor fault or data loss occurs. In the presented model, top-level wind-induced displacement in the time and frequency domains, and wind data in the frequency domain are configured into the input map of the CNN to reflect the resisting capacity of a tall building, the change in the dynamic characteristics of the building due to wind loads, and the relationship between wind load and the building. To evaluate stress, which is used as a safety indicator for structural members in the building, the maximum and minimum strains of columns are set as the output layer of the CNN. The CNN is trained using measured wind and wind response data to predict the column strains during a future wind load. The presented model is validated using data from a wind tunnel test of a building model. The performance of the presented model is verified through strain estimation with data that were not used in the CNN training. To assess the validity of the presented input map configuration, the estimation performance is compared with a CNN that considered only the time domain responses as input. Furthermore, the effects of the variations in the configuration of the CNN on the wind response estimation performance are examined.
|Number of pages||16|
|Journal||Computer-Aided Civil and Infrastructure Engineering|
|Publication status||Published - 2019 Oct 1|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2011-0018360 and No. 2018R1A5A1025137).
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Computer Science Applications
- Computer Graphics and Computer-Aided Design
- Computational Theory and Mathematics