Abstract
Compared to the past, the complexity of construction-project progress has increased as the size of structures has become larger and taller. This has resulted in many unexpected problems with an increasing frequency of occurrence, such as various uncertainties and risk factors. Recently, research was conducted to solve the problem via integration with data-collection automation tools of construction-project-progress measurement. Most of the methods used spatial sensing technology. Thus, this study performed a review of the representative technologies applied to construction-project-progress data collection and identified the unique characteristics of each technology. The basic principle of the progress proposed in this study is its execution through the point cloud and the attributes of BIM, which were studied in five stages: (1) Acquisition of construction completion data using a point cloud, (2) production of a completed 3D model, (3) interworking of an as-planned BIM model and as-built model, (4) construction progress tracking via overlap of two 3D models, and (5) verification by comparison with actual data. This has confirmed that the technical limitations of the construction progress tracking through the point cloud do not exist, and that a fairly high degree of progress data which contains efficiency and accuracy can be collected.
Highlights
Data related to the progress of construction projects are very useful both to determine whether timelines are being kept and to assess the quality of the work done, and these data are essential to improving the productivity of construction management [1,2]
This result corresponded to a difference of less than 3% compared with the actual data
With respect to for data acquisition, the drone- and LIDAR-based point cloud data acquisition methods were examined, and the accuracy of data was verified with respect to their application to actual construction projects
Summary
Data related to the progress of construction projects are very useful both to determine whether timelines are being kept and to assess the quality of the work done, and these data are essential to improving the productivity of construction management [1,2]. The progress of construction projects is currently tracked in various ways, such as scheduling, utilizing construction methods, expenditure management, and resource/quality management, and it is difficult to accurately track and record all of those activities [3]. The second one is information related to the current construction progress. The latter type cannot be collected, and continuously changes. For most construction project sites, data acquisition depends on the manual recording of information on paper, and the use of photos and documents causes many constraints in time and space. Automation is considered to be the most economical solution to these data acquisition-related problems [4,5]
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