TY - GEN
T1 - Characterizing point cloud data density for spatial change-based maintenance planning of civil infrastructure systems
AU - Chen, Jiawei
AU - Tang, Pingbo
AU - Xiong, Wen
N1 - Funding Information:
This material is based on work supported by the National Science Foundation (NSF) under Grant No. 1454654. NSF’s support is gratefully acknowledged. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NSF.
Publisher Copyright:
© 2018 American Society of Civil Engineers (ASCE). All rights reserved.
PY - 2018
Y1 - 2018
N2 - Understanding the impacts of point-cloud data density on the reliability of change detection calculations is crucial for maintenance planning of civil infrastructure systems. Automated spatial change detection of structures (e.g., deformations of bridges) based on laser scanning data can help engineers assess structural conditions and prioritize maintenance activities. Collecting point cloud data with high accuracy and level of detail is time-consuming and difficult due to complexed environments on construction sites. An efficient method that evaluates the impacts of data quality on change detection is thus important for inspectors. Inspectors could collect point clouds with relatively low data densities to save time for data collection and data processing without compromising domain requirements of change detection in engineering projects. As a step toward comprehending 3D data quality on spatial change analysis of structures, this paper examines an automated 3D data quality checking method to quantify the impacts of point cloud data density on the reliability of spatial change detection of civil infrastructures. The authors designed five comparisons for supporting the impact analysis of data density. In the five comparisons, the authors used the same reference point cloud and five point clouds with different data densities. The authors sub sampled the point clouds to make them having lower, similar, and higher data densities than those of the reference point cloud. Based on the change detection results, the authors found that when the data density of the point cloud is similar as that of the reference point cloud, the change detection method detects most of the changed areas. When data density of the compared point cloud is much higher than that of the reference point cloud, the area of the detected changes increases a little but it takes significantly more time to collect and process the data. The future research will further characterize the impacts of data noise, accuracy, and data completeness on the reliability of change analysis of structures of various geometric complexities.
AB - Understanding the impacts of point-cloud data density on the reliability of change detection calculations is crucial for maintenance planning of civil infrastructure systems. Automated spatial change detection of structures (e.g., deformations of bridges) based on laser scanning data can help engineers assess structural conditions and prioritize maintenance activities. Collecting point cloud data with high accuracy and level of detail is time-consuming and difficult due to complexed environments on construction sites. An efficient method that evaluates the impacts of data quality on change detection is thus important for inspectors. Inspectors could collect point clouds with relatively low data densities to save time for data collection and data processing without compromising domain requirements of change detection in engineering projects. As a step toward comprehending 3D data quality on spatial change analysis of structures, this paper examines an automated 3D data quality checking method to quantify the impacts of point cloud data density on the reliability of spatial change detection of civil infrastructures. The authors designed five comparisons for supporting the impact analysis of data density. In the five comparisons, the authors used the same reference point cloud and five point clouds with different data densities. The authors sub sampled the point clouds to make them having lower, similar, and higher data densities than those of the reference point cloud. Based on the change detection results, the authors found that when the data density of the point cloud is similar as that of the reference point cloud, the change detection method detects most of the changed areas. When data density of the compared point cloud is much higher than that of the reference point cloud, the area of the detected changes increases a little but it takes significantly more time to collect and process the data. The future research will further characterize the impacts of data noise, accuracy, and data completeness on the reliability of change analysis of structures of various geometric complexities.
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U2 - 10.1061/9780784481264.076
DO - 10.1061/9780784481264.076
M3 - Conference contribution
AN - SCOPUS:85049177955
T3 - Construction Research Congress 2018: Construction Information Technology - Selected Papers from the Construction Research Congress 2018
SP - 776
EP - 785
BT - Construction Research Congress 2018
A2 - Wang, Chao
A2 - Berryman, Charles
A2 - Harris, Rebecca
A2 - Harper, Christofer
A2 - Lee, Yongcheol
PB - American Society of Civil Engineers (ASCE)
T2 - Construction Research Congress 2018: Construction Information Technology, CRC 2018
Y2 - 2 April 2018 through 4 April 2018
ER -