Activity-level construction progress monitoring through semantic segmentation of 3D-informed orthographic images

Aritra Pal, Jacob J. Lin, Shang Hsien Hsieh, Mani Golparvar-Fard

Research output: Contribution to journalArticlepeer-review


Effective progress monitoring is crucial for successful construction project delivery. Visual data, comprising images and videos of construction operations, has emerged as a valuable source for gaining comprehensive insights into project status. While various vision-based methods have been developed for automated progress monitoring at the element level (e.g., columns, beams, floors, walls), challenges persist in achieving accurate schedule activity-level (e.g., formwork, reinforcement, concrete placement) progress tracking. Existing methods often struggle to report progress status beyond binary form (built/not built). To address these limitations, this research proposes a novel framework for precisely measuring the percent completion of activities associated with under-construction building elements. The framework is implemented through an Activity-level progress monitoring system (ALPMS). ALPMS takes the input of construction site images and a four-dimensional building information model (BIM) and outputs the activity-wise completion percentage required for updating project schedules. It creates as-built point clouds from images, compares them with the as-planned BIM, generates orthographic views of under-construction elements through projective transformation or neural radiance fields (NeRF), applies deep learning-based semantic segmentation for progress reasoning, and estimates completion percentages. Finally, the activity-level rich semantic information is transferred to the as-built point cloud and BIM for three-dimensional visualization of the progress status. When the framework was applied to two building construction projects, on average, the ALPMS could report the activity-wise completion percentages with <6% mean absolute error. Future research shall focus on automating the mapping of project schedule activities and activity-level progress details for timely schedule updates and prediction of project completion dates.

Original languageEnglish (US)
Article number105157
JournalAutomation in Construction
StatePublished - Jan 2024
Externally publishedYes


  • Completion percentages
  • Computer vision
  • Construction progress monitoring
  • Deep learning
  • Earned value management (EVM)
  • Neural radiance field (NeRF)
  • Orthographic view synthesis
  • Projective transformation
  • Schedule activity
  • Semantic segmentation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction


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