In Myanmar, defects and possible deformation were reported in many long-span suspension bridges. The current state of bridge infrastructure must be inspected, so that deterioration can be stalled and failure can be prevented. A 3D laser scanner, specifically the terrestrial laser scanner (TLS), has demonstrated the ability to capture surface geometry with millimeter accuracy. Consequently, TLS technology has received significant interest in various applications including in the field of structural survey. However, research on its application in large bridge structure remains limited. This study examines the use of TLS point cloud for the measurement of three deformation behaviors at the Pathein Suspension Bridge in Myanmar. These behaviors include tower inclination, hanger inclination, and deflection of bridge truss. The measurement results clearly captured the deformation state of the bridge. A comparison of the measurement results with available conventional measurements yielded overall agreement. However, errors were observed in some areas, which could be due to noise and occlusion in the point cloud model. In this study, the advantages of TLS in providing non-discrete data, direct measurement in meaningful unit, and access to difficult-to-access sections, such as top of towers or main cables, were demonstrated. The limitations of TLS as observed in this study were mainly influenced by external factors during field survey. Hence, it was suggested that further study on appropriate TLS surveying practice for large bridge structure should be conducted.

1. [1] Japan International Cooperation Agency, “Research Study on Review and Application of the Bridge Engineering Training Centre Project in Myanmar,” Technical report, 2012.
2. [2] Japan Infrastructure Partners, “Current Situation and Issues of Myanmar ’ s Bridge Work,” Technical report, Japan Infrastructure Partners, 2012, http://www.infra-jip.or.jp/H23BridgeMaintenanceReportenglish.pdf [accessed Sep. 17, 2016]
3. [3] H. S. Park, H. M. Lee, H. Adeli, and I. Lee, “A New Approach for Health Monitoring of Structures: Terrestrial Laser Scanning,” Computer-Aided Civil and Infrastructure Engineering, Vol.22, No.1, pp. 19–30, 2007, doi:10.1111/j.1467-8667.2006.00466.x.
4. [4] W. Wang, W. Zhao, L. Huang, V. Vimarlund, and Z. Wang, “Applications of terrestrial laser scanning for tunnels: a review,” Journal of Traffic and Transportation Engineering (English Edition), Vol.1, No.5, pp. 325–337, 2014, doi:10.1016/S2095-7564(15)30279-8.
5. [5] Y.-J. Cheng, W. Qiu, and J. Lei, “Automatic Extraction of Tunnel Lining Cross-Sections from Terrestrial Laser Scanning Point Clouds,” Sensors, Vol.16, No.10, pp. 1648, 2016, http://www.mdpi.com/1424-8220/16/10/1648, doi:10.3390/s16101648.
6. [6] T. Mill, A. Alt, and R. Liias, “Combined 3D building surveying techniques terrestrial laser scanning (TLS) and total station surveying for BIM data management purposes,” Journal of Civil Engineering and Management, Vol.19, No.sup1, pp. S23–S32, 2013, http://www.tandfonline.com/doi/abs/10.3846/13923730.2013.795187, doi:10.3846/13923730.2013.795187.
7. [7] V. Kankare, M. Holopainen, M. Vastaranta, E. Puttonen, X. Yu, J. Hyyppä, M. Vaaja, H. Hyyppä, and P. Alho, “Individual tree biomass estimation using terrestrial laser scanning,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol.75, pp. 64–75, 2013, http://dx.doi.org/10.1016/j.isprsjprs.2012.10.003, doi:10.1016/j.isprsjprs.2012.10.003.
8. [8] S. Srinivasan, S. C. Popescu, M. Eriksson, R. D. Sheridan, and N. W. Ku, “Multi-temporal terrestrial laser scanning for modeling tree biomass change,” Forest Ecology and Management, Vol.318, pp. 304–317, 2014, http://dx.doi.org/10.1016/j.foreco.2014.01.038, doi:10.1016/j.foreco.2014.01.038.
9. [9] A. Pesci, G. Casula, and E. Boschi, “Laser scanning the Garisenda and Asinelli towers in Bologna (Italy): Detailed deformation patterns of two ancient leaning buildings,” Journal of Cultural Heritage, Vol.12, No.2, pp. 117–127, 2011, http://dx.doi.org/10.1016/j.culher.2011.01.002, doi:10.1016/j.culher.2011.01.002.
10. [10] M. J. Olsen, F. Kuester, B. J. Chang, and T. C. Hutchinson, “Terrestrial Laser Scanning-Based Structural Damage Assessment,” Journal of Computing in Civil Engineering, Vol.24, No.3, pp. 264–272, 2010, doi:10.1061/(ASCE)CP.1943-5487.0000028.
11. [11] G. Teza, A. Pesci, and A. Ninfo, “Morphological Analysis for Architectural Applications: Comparison between Laser Scanning and Structure-from-Motion Photogrammetry,” Journal of Surveying Engineering, Vol.142, No.2014, pp. 1–10, 2016, doi:10.1061/(ASCE)SU.1943-5428.0000172.
12. [12] W. Liu, S. Chen, and E. Hauser, “LiDAR-based Bridge Structure Defect Detection,” pp. 27–34, 2011, doi:10.1111/j.1747-1567.2010.00644.x.
13. [13] A. Pesci, G. Teza, E. Bonali, G. Casula, and E. Boschi, “A Laser Scanning-based Method for Fast Estimation of Seismic-induced Building Deformations,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol.79, pp. 185–198, 2013, doi:10.1016/j.isprsjprs.2013.02.021.
14. [14] P. Oskouie, B. Becerik-Gerber, and L. Soibelman, “Automated Measurement of Highway Retaining Wall Displacements Using Terrestrial Laser Scanners,” Automation in Construction, Vol.65, pp. 86–101, 2016, http://dx.doi.org/10.1016/j.autcon.2015.12.023, doi:10.1016/j.autcon.2015.12.023.
15. [15] S. Sotoodeh, “Outlier Detection in Laser Scanner Point Clouds,” Iaprs, Vol.XXX, No.Part 5, pp. 297–302, 2006.
16. [16] R. Wahl and R. Klein, “Efficient RANSAC for Point-Cloud Shape Detection,” Computer Graphics Forum, Vol.26, No.2, pp. 214–226, 2007.
17. [17] T. Landes and P. Grussenmeyer, “Hough-Transform and Extended RANSAC Algorithms for Automatic Detection of 3D Building Roof from LiDAR Data,” Vol.36, No.1, pp. 407–412, 2007.
18. [18] Construction Myanmar Ministry of, “Pathein Report,” Technical report, 2016.