Structural Parameter Identification of Ancient Stone Arch Bridge via Three-Dimensional Laser Ranger Scanning
Publication: Journal of Performance of Constructed Facilities
Volume 36, Issue 5
Abstract
Stone arch bridges are symbols of a shared civil engineering heritage worldwide, but they suffer from damage caused by various natural disasters that have occurred during their long service. The basis of ancient bridge preservation is the current situation assessment. This study proposes a structural parameter identification method for stone arch bridges via three-dimensional (3D) laser scanning. An automatic algorithm of arch axis extraction from massive point cloud data is developed and applied to the 800-year-old Lugou Bridge (LGB) in Beijing, and its structural parameters are identified from the extracted arch axis. The results reveal that the LGB’s arch axis is close to the arc curve. The axis extraction is accurate and the identification method is reliable, when compared to field measurement. The relative deformation between the upstream and downstream sides of each arch and the relative deviations of the clear rise and span between two longitudinally symmetric arches are manifestly large. This study extends the current identification methods for bridge heritage based on a 3D point cloud and provides a useful basis upon which to protect ancient arch bridges.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This work was supported by the National Key R&D Program of China (Grant No. 2019YFC1520800) and the National Natural Science Foundation of China (Grant Nos. 51978033 and 51878027). The authors thank those teachers and students of Beijing University of Civil Engineering and Architecture who participated in the scanning work. The authors also thank the staff of the Lugou Bridge Cultural Tourism Area for their help. We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.
References
Alamdari, M. M., L. Ge, K. Kildashti, Y. Zhou, B. Harvey, and Z. Du. 2019. “Non-contact structural health monitoring of a cable-stayed bridge: Case study.” Struct. Infrastruct. Eng. 15 (8): 1119–1136. https://doi.org/10.1080/15732479.2019.1609529.
Bayraktar, A., and E. Hökelekli. 2021. “Seismic performances of different spandrel wall strengthening techniques in masonry arch bridges.” Int. J. Archit. Heritage 15 (11): 1722–1740. https://doi.org/10.1080/15583058.2020.1719234.
Cha, G., S. Park, and T. Oh. 2019. “A terrestrial LiDAR-based detection of shape deformation for maintenance of bridge structures.” J. Constr. Eng. Manage. 145 (12): 04019075. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001701.
Diao, S., H. Yang, Y. Xiang, L. Wu, and X. Chen. 2021. “Research on splicing method of point cloud with insufficient features based on spatial reference.” J. Electron. Imaging 30 (4): 043008. https://doi.org/10.1117/1.JEI.30.4.043008.
Gan, L., and S. He. 2019. “Research on splicing method of low overlap point cloud.” [In Chinese.] Laser J. 40 (3): 84–90.
Guo, F. Q., and Z. W. Yu. 2011. “Remaining service life prediction of reinforced stone arch bridge.” In Vols. 90–93 of Applied mechanics and materials, 1127–1131. Freienbach, Switzerland: Trans Tech Publications. https://doi.org/10.4028/www.scientific.net/AMM.90-93.1127.
Harper, D. 2013. China. Footscray, VIC, Australia: Lonely Planet.
Kwiatkowski, J., W. Anigacz, and D. Beben. 2020. “Comparison of non-destructive techniques for technological bridge deflection testing.” Materials (Basel, Switzerland) 13 (8): 1908. https://doi.org/10.3390/ma13081908.
Lin, W., and T. Yoda. 2017. Bridge engineering: Classifications, design loading, and analysis methods. Amsterdam, Netherlands: Butterworth-Heinemann.
Lõhmus, H., A. Ellmann, S. Märdla, and S. Idnurm. 2018. “Terrestrial laser scanning for the monitoring of bridge load tests—Two case studies.” Surv. Rev. 50 (360): 270–284. https://doi.org/10.1080/00396265.2016.1266117.
Lv, C., W. Lin, and B. Zhao. 2021. “Approximate intrinsic voxel structure for point cloud simplification.” IEEE Trans. Image Process. 30 (Aug): 7241–7255. https://doi.org/10.1109/TIP.2021.3104174.
McNeely, D. K., G. C. Archer, and K. N. Smith. 1989. “Structural analysis of old stone arch bridges.” Can. J. Civ. Eng. 16 (6): 789–797. https://doi.org/10.1139/l89-122.
Oskoui, E. A., T. Taylor, and F. Ansari. 2019. “Method and monitoring approach for distributed detection of damage in multi-span continuous bridges.” Eng. Struct. 189 (Jun): 385–395. https://doi.org/10.1016/j.engstruct.2019.02.037.
Park, H. S., H. M. Lee, H. Adeli, and I. Lee. 2007. “A new approach for health monitoring of structures: Terrestrial laser scanning.” Comput.-Aided Civ. Infrastruct. Eng. 22 (1): 19–30. https://doi.org/10.1111/j.1467-8667.2006.00466.x.
Pipinato, A. 2015. Innovative bridge design handbook: Construction, rehabilitation and maintenance. Amsterdam, Netherlands: Butterworth-Heinemann.
Raposo, C., and J. P. Barreto. 2017. “Using 2 point+normal sets for fast registration of point clouds with small overlap.” In Proc., 2017 IEEE Int. Conf. on Robotics and Automation (ICRA), 5652–5658. New York: IEEE.
Riveiro, B., P. Morer, P. Arias, and I. de Arteaga. 2011. “Terrestrial laser scanning and limit analysis of masonry arch bridges.” Constr. Build. Mater. 25 (4): 1726–1735. https://doi.org/10.1016/j.conbuildmat.2010.11.094.
Sánchez-Rodríguez, A., B. Riveiro, B. Conde, and M. Soilán. 2018. “Detection of structural faults in piers of masonry arch bridges through automated processing of laser scanning data.” Struct. Control Health Monit. 25 (3): e2126. https://doi.org/10.1002/stc.2126.
Shao, X. 2004. Bridge engineering. Beijing: China Communications Press.
Solla, M., B. Riveiro, H. Lorenzo, and J. Armesto. 2014. “Ancient stone bridge surveying by ground-penetrating radar and numerical modeling methods.” J. Bridge Eng. 19 (1): 110–119. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000497.
Tang, P., and B. Akinci. 2012. “Automatic execution of workflows on laser-scanned data for extracting bridge surveying goals.” Adv. Eng. Inf. 26 (4): 889–903. https://doi.org/10.1016/j.aei.2012.07.004.
Wang, G., L. Wu, Y. Hu, and M. Song. 2021. “Point cloud simplification algorithm based on the feature of adaptive curvature entropy.” Meas. Sci. Technol. 32 (6): 065004. https://doi.org/10.1088/1361-6501/abd497.
Ye, C., S. Acikgoz, S. Pendrigh, E. Riley, and M. J. DeJong. 2018. “Mapping deformations and inferring movements of masonry arch bridges using point cloud data.” Eng. Struct. 173 (Oct): 530–545. https://doi.org/10.1016/j.engstruct.2018.06.094.
Yuan, S., W. Chen, F. Yang, T. He, and G. Liu. 2019. “Reinforcement analysis of rigid hangers for existing old arch bridges: A case study of Ling Bridge.” Int. J. Steel Struct. 19 (6): 1743–1754. https://doi.org/10.1007/s13296-019-00242-2.
Zhang, Q., L. Sui, Y. Gan, G. Xiao, and G. Qi. 2015. “Datum transformation parameters estimation and long-term evolution analysis of WGS84 and ITRS.” [In Chinese.] Geomatics Inf. Sci. Wuhan Univ. 40 (3): 395–400.
Zhou, Y., Z. Xiang, X. Zhang, Y. Wang, D. Han, and C. Ying. 2022. “Mechanical state inversion method for structural performance evaluation of existing suspension bridges using 3D laser scanning.” Comput.-Aided Civ. Infrastruct. Eng. 37 (5): 650–665. https://doi.org/10.1111/mice.12765.
Ziolkowski, P., J. Szulwic, and M. Miskiewicz. 2018. “Deformation analysis of a composite bridge during proof loading using point cloud processing.” Sensors (Basel) 18 (12): 4332. https://doi.org/10.3390/s18124332.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 36 • Issue 5 • October 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Dec 28, 2021
Accepted: May 6, 2022
Published online: Jul 13, 2022
Published in print: Oct 1, 2022
Discussion open until: Dec 13, 2022
Authors
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.