Sensor data reconstruction using bidirectional recurrent neural network with application to bridge monitoring |
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| Affiliation: | 1. Department of Civil and Environmental Engineering, Stanford University, 473 Via Ortega, Stanford, CA 94305-4020, USA;2. Department of Civil and Environmental Engineering, University of Michigan, 2350 Hayward St., Ann Arbor, MI 48109-2125, USA;3. Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea;1. A-818 Anzhong Building, College of Civil Engineering and Architecture, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, China;2. A-821 Anzhong Building, College of Civil Engineering and Architecture, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, China;1. Dept. of Civil and Environmental Engineering, Rice University, Houston, TX 77005, USA;2. Dept. of Mechanical Engineering, Rice University, Houston, TX 77005, USA;1. Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA;2. Department of Civil Engineering, McGill University, Montreal, Quebec H3A0G4, Canada;3. Department of Civil and Environmental Engineering, MIT, Cambridge, MA 02139, USA;1. School of Civil Engineering, Dalian University of Technology, Dalian 116023, China;2. School of Civil Engineering, Shenyang Jianzhu University, Shenyang 110168, China;1. #309 at College of Civil Engineering, Iran University of Science and Technology, Farjam St., Narmak. P.O. Box: 1684613114, Tehran, Iran;2. College of Civil Engineering, Iran University of Science and Technology, Iran;3. Department of Electrical Engineering, University of South Florida, United States |
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| Abstract: | Sensors are now commonly employed for monitoring and controlling of engineering systems. Despite significant advances in sensor technologies and their reliability, sensor fault is inevitable. Sensor data reconstruction methods have been studied to recover the missing or faulty sensor data, as well as to enable sensor fault detection and identification. Most existing sensor data reconstruction methods use only the spatial correlations among the sensor data, but they rarely consider the temporal correlations among the data. Use of temporal correlations among the sensor data can potentially improve the accuracy for reconstructing the data. This paper presents a data-driven bidirectional recurrent neural network (BRNN) for sensor data reconstruction, taking into consideration the spatiotemporal correlations among the sensor data. The methodology is demonstrated using the sensor data collected from the Telegraph Road Bridge located along the I-275 Corridor in Michigan. The results show that the BRNN-based method performs better than other current data-driven methods for accurately reconstructing the sensor data. |
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| Keywords: | Sensor data reconstruction Artificial neural network Bidirectional recurrent neural network Machine learning Structural health monitoring Smart structure |
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