TY - GEN
T1 - Examination of LiDAR scanning for quantitative structural assessments of highway bridges
AU - Trias, A.
AU - Gong, J.
AU - Moon, F.
N1 - Publisher Copyright:
© 2019 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings. All rights reserved.
PY - 2019
Y1 - 2019
N2 - The use of LiDAR in the realm of bridge assessment has been primarily limited to measuring large-scale bridge dimensions (greater than 2m), which have been successfully captured with relatively small errors (< 1%). The objective of this paper is to explore the capability of LiDAR technology to estimate smaller dimensions such as flange thickness, flange width, and girder depth. To satisfy these objectives, an eleven-span steel girder bridge carrying a highly transited highway was subjected to a series of LiDAR scans under normal operating conditions. Various dimensional quantities were then extracted from the data both directly from the point cloud and through a standard plane-fitting approach. The direct point cloud approach resulted on percent errors of up to 30% (compared to bridge plans) while the plane fitting method resulted on percent errors of up to 17%. The ability of LiDAR sensors to accurately characterize geometric information opens up new opportunities for their use as a means for in bridge surveying and structural health monitoring.
AB - The use of LiDAR in the realm of bridge assessment has been primarily limited to measuring large-scale bridge dimensions (greater than 2m), which have been successfully captured with relatively small errors (< 1%). The objective of this paper is to explore the capability of LiDAR technology to estimate smaller dimensions such as flange thickness, flange width, and girder depth. To satisfy these objectives, an eleven-span steel girder bridge carrying a highly transited highway was subjected to a series of LiDAR scans under normal operating conditions. Various dimensional quantities were then extracted from the data both directly from the point cloud and through a standard plane-fitting approach. The direct point cloud approach resulted on percent errors of up to 30% (compared to bridge plans) while the plane fitting method resulted on percent errors of up to 17%. The ability of LiDAR sensors to accurately characterize geometric information opens up new opportunities for their use as a means for in bridge surveying and structural health monitoring.
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M3 - Conference contribution
AN - SCOPUS:85091459380
T3 - 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings
SP - 201
EP - 206
BT - 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure
A2 - Chen, Genda
A2 - Alampalli, Sreenivas
PB - International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII
T2 - 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019
Y2 - 4 August 2019 through 7 August 2019
ER -