Process-oriented progress monitoring of cast-in-place shell constructions based on computer vision

J. Schlenger, S. Vilgertshofer, A. Borrmann

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

Automated progress monitoring builds an important foundation for objective productivity analysis of construction processes. Digital twins of the construction phase rely on fully automated approaches to acquire near real-time progress information. This is essential for identifying bottlenecks during construction and supporting future project planning. Many existing vision-based methods lack automated image acquisition, fast computation times, or fine-grained progress information. This paper presents a new vision-based construction monitoring approach that reduces the geometric information provided in exchange for a higher time resolution and a higher level of automation. Instead of the detailed geometry, the real-time status of the building elements is provided. It is applied to cast-in-place concrete columns, identifying individual operational steps. The approach is based on projecting building elements from a building model onto images of a fixed on-site camera to then classify them according to the current element status with the help of a CNN. Using image sequences additionally allows accounting for moving objects and other outliers, which makes the approach robust and reliable.

Original languageEnglish
StatePublished - 2023
Event30th International Conference on Intelligent Computing in Engineering 2023, EG-ICE 2023 - London, United Kingdom
Duration: 4 Jul 20237 Jul 2023

Conference

Conference30th International Conference on Intelligent Computing in Engineering 2023, EG-ICE 2023
Country/TerritoryUnited Kingdom
CityLondon
Period4/07/237/07/23

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