Remote sensing of environment and disaster laboratory

Institute of Industrial Science, the University of Tokyo, Japan

Bridge Tower Displacement Assessment based on Dense
Structure-from-Motion Point Clouds:
Application to Twantay Bridge, Myanmar
Nuntikorn KITRATPORN, Wataru TAKEUCHI, Koji MATSUMOTO, Kohei NAGAI
Institute of Industrial Science, the University of Tokyo, Japan
Abstract: Timely inspection of bridge is critical to stall deterioration and prevent its failure. In remote areas and developing countries, inspection resources are notavailable resulting in low or no regular inspection. A low-cost and scalable method is needed. Structure-from-Motion technique with consumer-grade camera togenerate 3D dense point clouds (DSfM) can be a suitable solution. This study aims to answer whether DSfM is capable of assessing bridge tower inclination inactual field application. 3-steps processing were proposed: 1) DSfM reconstruction, 2) Change detection between DSfM and 3D blueprint, and 3) Edge extraction tominimize noise. Results were compared with measurement from terrestrial laser scanner (TLS) and a provided Finite Element Model (FEM) analysis. DSfM cancapture structure's geometry and provide displacement trends with RMSE under 3.2cm. Accuracy of DSfM method was shown to be impacted by registration errors.
Centimeter level of accuracy may be applicable for initial screening to further allocate advance resources. Accuracy improvement is subjected for future study.

1. BACKGROUND

3. RESULTS

Twantay Suspension Bridge
Towers incline toward deck, no
direct check & limited resources.

Inclination Results• Highest change from South tower in y-axis• Facets (x-direction), larger than blueprint as height increases• DSfM  Low-cost/scalable◄ incline signs
Can it be used?
(bending towers,• Objective:
applyDSfM
concrete crack,technique for assessing bridge
expansion join)
tower displacement in actual
field limitation• Significant: Promote inspection
to ensure infrastructure health  continuous economic activity  SDG

2. METHODOLOGYSONY DSC-RX100M3
1px = 1.43cm

DSfM
Reconstruction

Scaling

Rotate points toensure piererectedstraight

Noise Removal

2.1 DSfM Reconstruction
Register DSfM toBlueprint Mesh

Change for eachpoint

2.2 Change between DSfM and Bluerpint
min/max x andaverage change

RANSAC

Rotate points(pier not deform)

2.3 Edge Extraction
FARO Focus3D X330
Mean error <0.63cm

Compare DSfM
vs TLS vs. FEM

Comparison:DSfM vs. TLS vs. FEM
RMS error considered TSL as most accurate◄ local coordinate: thisDSfM (original)
DSfM (rotated)
FEM

RMSE (cm)N1 N2 S1 S2
1.1 1.5 5.1 7.5
3 6.4 3.2 2.7
4.5 5.4 1.2 4.9

study
usedlocal
coordinate by transformall data into it2.1v

Sparse point cloud created from 2D images

DSfM Model of South tower

2.32.2• Error from registration (ICP) can mislead the result (south tower)  target• Discrepancy between blueprint drawing and as-built condition impact thev
outcome  time-series with multiple epoch• Higher camera model (require training for operator) should be tested• Integration with multiple sensors (TLS + DSfM) and identifying appropriate
application should be studied

Cloud-to-Cloud

Cloud-to-Mesh
Registration (ICP)

Distance difference between
reference and DSfM

4. DISCUSSION & FUTURE WORK

Reduce surface noiseusing RANSAC andsearch for edge points

REFERENCEv

Lague, D., Brodu, N. and Leroux, J., Accurate 3D comparison of complex topography with terrestrial laser scanner : application
to the Rangitikei canyon (N-Z), 2013, ISPRS journal of Photogrammmetry and Remote SensingWahl, R. and Klein, R. Efficient RANSAC for Point Cloud Shape Detection. Computer Graphics Forum, 26(2). 214-226, 2007

For further details, contact: Nuntikorn Kitratporn, Be-601, 6-1, Komaba 4-chome, Meguro, Tokyo 153-8505 JAPAN (URL: http://wtlab.iis.u-tokyo.ac.jp/ E-mail: tita@iis.u-tokyo.ac.jp)