Characterizing point cloud data density for spatial change-based maintenance planning of civil infrastructure systems

Jiawei Chen, Pingbo Tang, Wen Xiong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationConstruction Research Congress 2018
Subtitle of host publicationConstruction Information Technology - Selected Papers from the Construction Research Congress 2018
PublisherAmerican Society of Civil Engineers (ASCE)
Pages776-785
Number of pages10
Volume2018-April
ISBN (Electronic)9780784481264
DOIs
StatePublished - Jan 1 2018
EventConstruction Research Congress 2018: Construction Information Technology, CRC 2018 - New Orleans, United States
Duration: Apr 2 2018Apr 4 2018

Other

OtherConstruction Research Congress 2018: Construction Information Technology, CRC 2018
CountryUnited States
CityNew Orleans
Period4/2/184/4/18

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ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction

Cite this

Chen, J., Tang, P., & Xiong, W. (2018). Characterizing point cloud data density for spatial change-based maintenance planning of civil infrastructure systems. In Construction Research Congress 2018: Construction Information Technology - Selected Papers from the Construction Research Congress 2018 (Vol. 2018-April, pp. 776-785). American Society of Civil Engineers (ASCE). https://doi.org/10.1061/9780784481264.076