Faulty data detection and classification for bridge structural health monitoring via statistical and deep‐learning approach
Summary Over the last several decades, a lot of bridges have been equipped with the bridge structural health monitoring system, leading to an accumulation of voluminous monitoring data. Since the sensors and associated transmission hardware are subjected to harsh environments, the monitoring data fr...
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Published in | Structural control and health monitoring Vol. 28; no. 11 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Pavia
Wiley Subscription Services, Inc
01.11.2021
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Subjects | |
Online Access | Get full text |
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Summary: | Summary
Over the last several decades, a lot of bridges have been equipped with the bridge structural health monitoring system, leading to an accumulation of voluminous monitoring data. Since the sensors and associated transmission hardware are subjected to harsh environments, the monitoring data frequently contains various faults, and it is laborious to cleanse the data manually. For the purpose of automatically detecting and classifying faulty monitoring data in large quantities, this paper proposes a novel method that uses the relative frequency distribution histograms (RFDH) of monitoring data as well as the one‐dimensional convolutional neural network (1‐D CNN). The overall procedure of this method can be described as follows: First, RFDHs are constructed from different classes of hour‐long data segments. Second, inverted envelopes of the RFDHs are labeled as the training data to train the 1‐D CNN. Third, a well‐trained 1‐D CNN is used to detect and classify long‐term monitoring data according to their RFDHs of hour‐long data segments. Comprehensive validation of the proposed method is conducted with selective acceleration data collected from two long‐span bridges. The validation yields satisfactory results, demonstrating the accuracy, efficiency, and generality of the method. |
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Bibliography: | Funding information National Natural Science Foundation of China, Grant/Award Number: 51978508; State Key Laboratory for Health and Safety of Bridge Structures, Grant/Award Number: BHSKL19‐10‐GF; Technology Cooperation Project of Shanghai Qizhi Institute, Grant/Award Number: SYXF0120020109; National Key R&D Program of China, Grant/Award Number: 2019YFB1600702 |
ISSN: | 1545-2255 1545-2263 |
DOI: | 10.1002/stc.2824 |