Spatial Characterization of Strain Variation in the Profile of Tunnel Structure Using Monitoring Data and Numerical Modeling
Publication: Journal of Infrastructure Systems
Volume 28, Issue 3
Abstract
The rapid proliferation of tunnel construction presents challenges to the safe operation of tunnels. Although Structural Health Monitoring Systems (SHMSs) have been widely used to prevent tunnel disasters, it is still impossible to record the mechanical behavior of the full profile of structures because of a limited number of monitoring points. Along this line, this study proposes a spatial deduction model based on a machine-learning algorithm to characterize the mechanical behavior of a tunnel structure profile driven by limited monitoring data. Strain variation is considered to reflect the mechanical behaviors of the structure, and the monitoring data obtained from the SHMS of Dinghuaimen Yangtze River tunnel are adopted for these experiments. First, the framework of the spatial deduction model, which uses a nonnegative matrix factorization (NMF) algorithm, is presented. Then, the model is formulated using the monitoring data. A numeric model is developed to reflect the geological conditions in the field to compare with the data-driven model, and the spatial deduction results are used to analyze the real-time and historical mechanical behaviors of the structure. The results indicate that the sensitive positions of the tunnel structure are the arch crown, hance, and inverted arch. The correlation between the deduction result and actual data is more than 85%, and the error is less than 2.7 με, so the presented model is reasonable.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grant Nos. U1806226, 51991395, and 51991392 and Key deployment projects of Chinese Academy of Sciences No. ZDRW-ZS-2021-3-3 and Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (No. 2019QZKK0904).
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Published In
Journal of Infrastructure Systems
Volume 28 • Issue 3 • September 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Nov 16, 2021
Accepted: May 7, 2022
Published online: Jul 7, 2022
Published in print: Sep 1, 2022
Discussion open until: Dec 7, 2022
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Cited by
- Xuyan Tan, Weizhong Chen, Jianping Yang, Xianjun Tan, Application of a Data-Driven Intelligent Information System in Infrastructure: Underwater Tunnel Case Study, Journal of Performance of Constructed Facilities, 10.1061/JPCFEV.CFENG-4046, 37, 1, (2023).