https://scholars.lib.ntu.edu.tw/handle/123456789/637342
標題: | Activity-level construction progress monitoring through semantic segmentation of 3D-informed orthographic images | 作者: | Pal, Aritra JACOB JE-CHIAN LIN SHANG-HSIEN HSIEH Golparvar-Fard, Mani |
關鍵字: | 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 | 公開日期: | 1-一月-2024 | 卷: | 157 | 來源出版物: | Automation in Construction | 摘要: | 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/637342 | ISSN: | 09265805 | DOI: | 10.1016/j.autcon.2023.105157 |
顯示於: | 土木工程學系 |
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