The array of GNSS for structure deformation monitoring

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Authors:


Trung Thanh Duong, orcid.org/0009-0005-9336-0949, Hanoi University of Mining and Geology, Faculty of Geomatics and land Administration, Hanoi, Socialist Republic of Vietnam

Long Quoc Nguyen*, orcid.org/0000-0002-4792-3684, Hanoi University of Mining and Geology, Faculty of Geomatics and land Administration, Hanoi, Socialist Republic of Vietnam; Hanoi University of Mining and Geology, Innovations for Sustainable and Responsible Mining (ISRM) Research Group, Hanoi, Socialist Republic of Vietnam

Duc Van Bui, orcid.org/0000-0003-1073-8060, Hanoi University of Mining and Geology, Faculty of Civil Engineering, Hanoi, Socialist Republic of Vietnam

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.


повний текст / full article



Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2025, (4): 168 - 176

https://doi.org/10.33271/nvngu/2025-4/168



Abstract:



Purpose.
To develop and implement the Global Navigation Satellite System (GNSS) array system for structural deformation monitoring (SDM) to ensure the safety and longevity of critical infrastructure such as bridges, dams, and high-rise buildings.


Methodology.
This research utilizes dual-antenna GNSS receivers to form a GNSS network with redundant measurements for real-time least-square adjustment. The methodology integrates GNSS with additional sensors, such as inclinometers, and employs advanced data processing techniques to mitigate errors and enhance accuracy. Real-time deformation measurements are achieved using multi-frequency, multi-constellation GNSS receivers with Real-Time Kinematic (RTK) and least-square estimation, combined with sensor fusion algorithms to provide a comprehensive view of structural behavior.


Findings.
A pilot deployment on a large-scale structure demonstrates the system’s capability to monitor displacements and long-term deformation trends with high spatial and temporal resolution. The validation results confirm the accuracy and reliability of the GNSS array compared to conventional monitoring methods.


Originality.
This study addresses the limitations of single-point GNSS solutions by deploying multiple receivers across a structure, offering a novel approach to real-time SDM. The integration of GNSS with auxiliary sensors and the application of advanced data processing techniques represent significant advancements in GNSS-based monitoring technologies.


Practical value.
The proposed GNSS array system offers a scalable, cost-effective solution for real-time SDM, addressing critical challenges in infrastructure safety and management. The findings pave the way for the widespread adoption of GNSS-based monitoring technologies in critical structural applications.



Keywords:
GNSS array, structure deformation monitoring, Least Squares Method, conditional constraint

References.


1. Qiu, Z., Jiao, M., Jiang, T., & Zhou, L. (2020). Dam Structure Deformation Monitoring by GB-InSAR Approach. IEEE Access, 8, 123287-123296. https://doi.org/10.1109/access.2020.3005343

2. Akhmedov, D., Boguspaev, N., Raskaliev, A., Samsonenko, A., & Zhumagali, S. (2023). Development of a simulation model for determining the coordinates of air objects based on the GNSS navigation signal reflected from the air object and received on the antenna of the navigation receiver. Engineering Journal of Satbayev University, 145(1), 32-38. https://doi.org/10.51301/ejsu.2023.i1.05

3. Baltiyeva, A., Orynbassarova, E., Zharaspaev, M., & Akhmetov, R. (2023). Studying sinkholes of the earth’s surface involving radar satellite interferometry in terms of Zhezkazgan field, Kazakhstan. Mining of Mineral Deposits, 17(4), 61-74. https://doi.org/10.33271/mining17.04.061

4. Lopez-Higuera, J. M., Rodriguez Cobo, L., Quintela Incera, A., & Cobo, A. (2011). Fiber Optic Sensors in Structural Health Monitoring. Journal of Lightwave Technology, 29(4), 587-608. https://doi.org/10.1109/JLT.2011.2106479

5. Bellone, T., Dabove, P., Manzino, A. M., & Taglioretti, C. (2014). Real-time monitoring for fast deformations using GNSS low-cost receivers. Geomatics. Natural Hazards and Risk, 7(2), 458-470. https://doi.org/10.1080/19475705.2014.966867

6. Shults, R., Ormambekova, A., Medvedskij, Y., & Annenkov, A. (2023). GNSS-Assisted Low-Cost Vision-Based Observation System for Deformation Monitoring. Applied Sciences, 13(5), 2813. https://doi.org/10.3390/app13052813

7. Yang, Y., Zheng, Y., Yu, W., Chen, W., & Weng, D. (2019). Deformation monitoring using GNSS-R technology. Advances in Space Research, 63(10), 3303-3314. https://doi.org/10.1016/j.asr.2019.01.033

8. Yu, Ln., Xiong, K., & Gao, Xf. (2024). Application of GNSS-PPP on Dynamic Deformation Monitoring of Offshore Platforms. China Ocean Engineering, 38, 352-361. https://doi.org/10.1007/s13344-024-0029-7

9. Xi, R. J., Zhou, X. H., Jiang, W. P., & Chen, Q. S., (2018). Simultaneous estimation of dam displacements and reservoir level variation from GPS measurements. Measurement, 122, 247-256. https://doi.org/10.1016/j.measurement.2018.03.036

10.      Leick, A., Rapoport, L., & Tatarnikov, D. (2015). GPS Satellite Surveying. https://doi.org/10.1002/9781119018612

11.      Xu, C., Chen, Y., & Qiao, L. (2017). Analysis of multipath effects in GNSS-based structural health monitoring. Advances in Space Research, 59(2), 567-578.

12.      Li, X., Zhang, Q., & Yuan, Y. (2018). Ionospheric delay corrections in GNSS structural monitoring systems: An overview and recent advances. Journal of Geodesy, 92(3), 281-299.

13.      Chen, X., Huang, J., & Zhao, Q. (2021). Optimization of GNSS sensor placement for large dam deformation monitoring. Structural Health Monitoring, 20(5), 1672-1691.

14.      Roberts, G. W., Meng, X., & Dodson, A. H. (2020). GNSS for bridge deformation monitoring: Dynamic analysis and integration with accelerometers. Engineering Structures, 234, 111782.

15.      Hou, C., Shi, J., Ouyang, C., Guo, J., & Zou, J. (2024). A dual-base station constraint method to improve deformation monitoring precision consistency in strip regions. Satellite Navigation, 5, 26. https://doi.org/10.1186/s43020-024-00148-3

16.      Li, R., Zhang, Z., Gao, Y., Zhang, J., & Ge, H. (2023). A New Method for Deformation Monitoring of Structures by Precise Point Positioning. Remote Sensing, 15(24), 5743. https://doi.org/10.3390/rs15245743

17.      Zhao, Z., Li, Y., Liu, C., & Gao, J. (2019). On-line part deformation prediction based on deep learning. Journal of Intelligent Manufacturing, 31(3), 561-574. https://doi.org/10.1007/s10845-019-01465-0

18.      Bui, K. T., Torres, J. F., Gutiérrez-Avilés, D., Nhu, V., Bui, D. T., & Martínez-Álvarez, F. (2022). Deformation forecasting of a hydropower dam by hybridizing a long short-term memory deep learning network with the coronavirus optimization algorithm. Computer-Aided Civil and Infrastructure Engineering, 37(11), 1368-1386. Portico. https://doi.org/10.1111/mice.12810

19.      Xie, Y., Meng, X., Wang, J., Li, H., Lu, X., Ding, J., Jia, Y., & Yang, Y. (2024). Enhancing GNSS Deformation Monitoring Forecasting with a Combined VMD-CNN-LSTM Deep Learning Model. Remote Sensing, 16(10), 1767. https://doi.org/10.3390/rs16101767

20.      Mao, W., Li, Z., Wang, Z., Li, P., Zhu, Y., & Hou, J. (2024). GNSS Ground deformation observation network optimization assisted using prior InSAR-derived ground surface deformation and multiscale iteration estimation. International Journal of Digital Earth, 17(1). https://doi.org/10.1080/17538947.2024.2329348

21.      Wang, G. (2023). A methodology for long-term offshore structural health monitoring using stand-alone GNSS: case study in the Gulf of Mexico. Structural Health Monitoring, 23(1), 463-478. https://doi.org/10.1177/14759217231169934

22.      Cina, A., Manzino, A. M., & Bendea, I. H. (2019). Improving GNSS Landslide Monitoring with the Use of Low-Cost MEMS Accelerometers. Applied Sciences, 9(23), 5075. https://doi.org/10.3390/app9235075

23.      Xin, S., Geng, J., Zeng, R., Zhang, Q., Ortega-Culaciati, F., & Wang, T. (2021). In-situ real-time seismogeodesy by integrating multi-GNSS and accelerometers. Measurement, 179, 109453. https://doi.org/10.1016/j.measurement.2021.109453

24.      Xie, Y., Zhang, S., Meng, X., Nguyen, D. T., Ye, G., & Li, H. (2024). An Innovative Sensor Integrated with GNSS and Accelerometer for Bridge Health Monitoring. Remote Sensing, 16(4), 607. https://doi.org/10.3390/rs16040607

25.      Shirzaei, M. (2023). A Kalman Filter Framework for Resolving 3D Displacement Field Time Series By Combining Multitrack Multitemporal InSAR and GNSS Horizontal Velocities. arXiv:2303, 03954, https://doi.org/10.48550/arXiv.2303.03954

26.      Shen, N., Chen, L., Liu, J., Wang, L., Tao, T., Wu, D., & Chen, R. (2019). A Review of Global Navigation Satellite System (GNSS)-Based Dynamic Monitoring Technologies for Structural Health Monitoring. Remote Sensing, 11(9), 1001. https://doi.org/10.3390/rs11091001

27.      Ghilani, C. D. (2017). Adjustment Computations: Spatial Data Analysis (6 th ed.). John Wiley & Sons.

28.      Pham Thi, H., Nghiem Quoc, D., Trinh Thi Hoai, T., Pham The, H., & Le Thi, N. (2019). Determination of the relationship between Vietnam national coordinate reference system (VN-2000) and ITRS, WGS84 and PZ-90. E3S Web of Conferences, 94, 03014. https://doi.org/10.1051/e3sconf/20199403014

 

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ISSN (print) 2071-2227,
ISSN (online) 2223-2362.
Journal was registered by Ministry of Justice of Ukraine.
Registration number КВ No.17742-6592PR dated April 27, 2011.

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