Byung Kwan OH

In this study, a structural response recovery method using a convolutional neural network is proposed. The aim of this study is to restore missing strain structural responses when they cannot be collected due to a sensor fault, data loss, or communication errors. To this end, a convolutional neural network model for data recovery is constructed using the strain monitoring data stably measured before the occurrence of data loss. Under the assumption that specific sensors fail among the multiple s

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 convolu

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 d