Technical Papers
Sep 27, 2020

Displacement Measurement Based on Data Fusion and Real-Time Computing

Publication: Journal of Performance of Constructed Facilities
Volume 34, Issue 6

Abstract

Displacement due to the deformation of civil structures such as bridges and buildings (caused by different external loads: e.g., vehicle, wind, and temperature) is an important factor for structure safety evaluation. The effectiveness of structure maintenance is highly dependent on the accuracy and reliability of the structural displacement measurement. An accelerometer is a tool that indirectly measures displacement and has gained widespread interest. However, the accuracy is questionable because noise exists in acceleration measurements and also can be undermined by accumulated errors during the double integration of the acceleration process. In this paper, a new displacement measurement algorithm based on data fusion technique was studied. In this algorithm, the Kalman filter was used as the fusion technique. The acceleration was taken as the primary measurement, and rotation measurement was used as the second measurement to minimize the effect of the error in the displacement prediction during the double integration of the acceleration process. To validate the performance and reliability of the algorithm, two lab tests were conducted: cantilever beam test and simply supported beam test. The results show that the proposed algorithm could minimize the effect of the accumulated error on the double integration of acceleration, thus providing a reliable estimation of the vertical displacement of a cantilever beam and simply supported beam.

Get full access to this article

View all available purchase options and get full access to this article.

Data Availability Statement

All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. The items are listed below:
1.
The test data from both the cantilever beam test and the simply supported beam test.
2.
The proposed displacement measurement algorithm Matlab code.

References

Bernmark, E., and C. Wiktorin. 2002. “A triaxial accelerometer for measuring arm movements.” Appl. Ergon. 33 (6): 541–547. https://doi.org/10.1016/S0003-6870(02)00072-8.
Burdet, O. 1998. “Automatic deflection and temperature monitoring of a balanced cantilever concrete bridge.” In Proc., 5th Int. Conf. of Short and Medium Span Bridges (No. EPFL-CONF-111633). Montréal: Canadian Society for Civil Engineering.
Drolet, L., F. Michaud, and J. Côté. 2000. “Adaptable sensor fusion using multiple Kalman filters.” In Vol. 2 of Proc., 2000 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2000), 1434–1439. New York: IEEE.
Foxlin, E. 1996. “Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter.” In Proc., IEEE 1996 Virtual Reality Annual Int. Symp., 185–194. New York: IEEE.
Gan, Q., and C. J. Harris. 2001. “Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion.” IEEE Trans. Aerosp. Electron. Syst. 37 (1): 273–279. https://doi.org/10.1109/7.913685.
Ha, D. W., H. S. Park, S. W. Choi, and Y. Kim. 2013. “A wireless MEMS-based inclinometer sensor node for structural health monitoring.” Sensors 13 (12): 16090–16104. https://doi.org/10.3390/s131216090.
Hamilton, W. R. 1848. “XI. On quaternions; or on a new system of imaginaries in algebra.” London, Edinburgh, Dublin Philos. Mag. J. Sci. 33 (219): 58–60. https://doi.org/10.1080/14786444808646046.
Hansson, G., P. Asterland, N. G. Holmer, and S. Skerfving. 2001. “Validity and reliability of triaxial accelerometers for inclinometry in posture analysis.” Med. Biol. Eng. Comput. 39 (4): 405–413. https://doi.org/10.1007/BF02345361.
Hwang, J., H. Yun, S. K. Park, D. Lee, and S. Hong. 2012. “Optimal methods of RTK-GPS/accelerometer integration to monitor the displacement of structures.” Sensors 12 (1): 1014–1034. https://doi.org/10.3390/s120101014.
James, D. 2006. Representing attitude: Euler angles, unit quaternions, and rotation vectors. Stanford, CA: Stanford Univ.
Liu, S., T. Qiu, Y. Qian, H. Huang, E. Tutumluer, and S. Shen. 2019. “Simulations of large-scale triaxial shear tests on ballast aggregates using sensing mechanism and real-time (SMART) computing.” Comput. Geotech. 110 (Jun): 184–198. https://doi.org/10.1016/j.compgeo.2019.02.010.
Luinge, H. J., and P. H. Veltink. 2004. “Inclination measurement of human movement using a 3-D accelerometer with autocalibration.” IEEE Trans. Neural Syst. Rehabil. Eng. 12 (1): 112–121. https://doi.org/10.1109/TNSRE.2003.822759.
Moreu, F., H. Jo, J. Li, R. E. Kim, S. Cho, A. Kimmle, and J. M. LaFave. 2015. “Dynamic assessment of timber railroad bridges using displacements.” J. Bridge Eng. 20 (10): 04014114. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000726.
Moschas, F., and S. Stiros. 2011. “Measurement of the dynamic displacements and of the modal frequencies of a short-span pedestrian bridge using GPS and an accelerometer.” Eng. Struct. 33 (1): 10–17. https://doi.org/10.1016/j.engstruct.2010.09.013.
Nakamura, S. I. 2000. “GPS measurement of wind-induced suspension bridge girder displacements.” J. Struct. Eng. 126 (12): 1413–1419. https://doi.org/10.1061/(ASCE)0733-9445(2000)126:12(1413).
Neto, P., J. N. Pires, and A. P. Moreira. 2013. “3-D position estimation from inertial sensing: Minimizing the error from the process of double integration of accelerations.” In Proc., IECON 2013—39th Annual Conf. of the IEEE Industrial Electronics Society, 4026–4031. New York: IEEE.
Park, J. W., S. H. Sim, and H. J. Jung. 2013. “Development of a wireless displacement measurement system using acceleration responses.” Sensors 13 (7): 8377–8392. https://doi.org/10.3390/s130708377.
Sasiadek, J. Z., and Q. Wang. 1999. “Sensor fusion based on fuzzy Kalman filtering for autonomous robot vehicle.” In Vol. 4 of Proc., 1999 IEEE Int. Conf. on Robotics and Automation (Cat. No. 99CH36288C), 2970–2975. New York: IEEE.
Sekiya, H., K. Kimura, and C. Miki. 2016. “Technique for determining bridge displacement response using MEMS accelerometers.” Sensors 16 (2): 257. https://doi.org/10.3390/s16020257.
Smyth, A., and M. Wu. 2007. “Multi-rate Kalman filtering for the data fusion of displacement and acceleration response measurements in dynamic system monitoring.” Mech. Syst. Sig. Process. 21 (2): 706–723. https://doi.org/10.1016/j.ymssp.2006.03.005.
Sun, S. L. 2004. “Multi-sensor optimal information fusion Kalman filters with applications.” Aerosp. Sci. Technol. 8 (1): 57–62. https://doi.org/10.1016/j.ast.2003.08.003.
Tamura, Y., M. Matsui, L. C. Pagnini, R. Ishibashi, and A. Yoshida. 2002. “Measurement of wind-induced response of buildings using RTK-GPS.” J. Wind Eng. Ind. Aerodyn. 90 (12–15): 1783–1793. https://doi.org/10.1016/S0167-6105(02)00287-8.
Willner, D., C. B. Chang, and K. P. Dunn. 1976. “Kalman filter algorithms for a multi-sensor system.” In Proc., 1976 IEEE Conf. on Decision and Control Including the 15th Symp. on Adaptive Processes, 570–574. New York: IEEE.
Xiong, C., H. Lu, and J. Zhu. 2017. “Operational modal analysis of bridge structures with data from GNSS/accelerometer measurements.” Sensors 17 (3): 436. https://doi.org/10.3390/s17030436.
Xu, N., S. Rangwala, K. K. Chintalapudi, D. Ganesan, A. Broad, R. Govindan, and D. Estrin. 2004. “A wireless sensor network for structural monitoring.” In Proc., 2nd Int. Conf. on Embedded Networked Sensor Systems, 13–24. New York: Association for Computing Machinery.
Yun, X., and E. R. Bachmann. 2006. “Design, implementation, and experimental results of a quaternion-based Kalman filter for human body motion tracking.” IEEE Trans. Rob. 22 (6): 1216–1227. https://doi.org/10.1109/TRO.2006.886270.

Information & Authors

Information

Published In

Go to Journal of Performance of Constructed Facilities
Journal of Performance of Constructed Facilities
Volume 34Issue 6December 2020

History

Received: Apr 20, 2020
Accepted: Jun 1, 2020
Published online: Sep 27, 2020
Published in print: Dec 1, 2020
Discussion open until: Feb 27, 2021

Permissions

Request permissions for this article.

Authors

Affiliations

Kun Zeng, M.ASCE [email protected]
Ph.D. Candidate, Dept. of Civil and Environmental Engineering, Pennsylvania State Univ., University Park, PA 16803. Email: [email protected]
Hai Huang, M.ASCE [email protected]
Associate Professor, Dept. of Rail Transportation Engineering, Pennsylvania State Univ., Altoona, PA 16601 (corresponding author). Email: [email protected]
Shubin Song [email protected]
Senior Engineer, Zhengzhou Road & Bridge Construction Investment Group Co., Ltd., Zheng-Shao Expressway Connection, West HangHai Rd., Erqi District, Zhengzhou, Henan 450000, China. Email: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share