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.
Bibliographical noteFunding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science, ICT and Future Planning, MSIP; Nos. 2011-0018360 and 2018R1A5A1025137).
The project on the US202/NJ23 highway overpass in Wayne has been realized with the important support, great help, and kind collaboration of several professionals and companies. The authors thank SMARTEC SA, Switzerland; Drexel University, in particular Professor Emin Aktan, Professor Frank Moon, and graduate student Jeff Weidner; New Jersey Department of Transportation (NJDOT); Long-Term Bridge Performance (LTBP) program of Federal Highway Administration; PB Americas, Inc., Lawrenceville, NJ, in particular Mr Michael S Morales, LTBP Site Coordinator; Rutgers University, in particular Professors Ali Maher and Nenad Gucunski; all IBS partners; and Kevin, the lift operator. The authors also thank Yao who helped with the sensor installation and Joseph Vocaturo for his design and creation of the sensor mounting equipment. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science, ICT and Future Planning, MSIP; Nos. 2011-0018360 and 2018R1A5A1025137).
© The Author(s) 2020.
All Science Journal Classification (ASJC) codes
- Mechanical Engineering