Convolutional neural network–based data recovery method for structural health monitoring

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 sensors installed on a structure, the structural responses of these specific sensors are intentionally excluded and the remaining structural responses are set as the input data of the convolutional neural network. In addition, the intentionally excluded structural responses are set as the output data of the convolutional neural network. In case of a sensor fault, the trained convolutional neural network is used to recover the missing strain responses using functional sensors alone. The applicability of the proposed method is verified by a numerical study on a beam structure and an experimental study on a frame structure. The data recovery performance of the proposed convolutional neural network is discussed according to the number of failed sensors and the types of structural members with the failed sensors. Finally, the field applicability of the proposed method is examined using strain monitoring data measured from an overpass bridge in use over a long period of time.