Automated Model-Based Recognition of Progress Using Daily Construction Photographs and IFC-Based 4D Models

Accurate and efficient tracking, analysis and visualization of the as-built status of projects are critical components of a successful project monitoring. Such information directly supports control decision-making and if automated, can significantly impact management of a project. In this paper, we describe an innovative automated approach for recognition of physical progress from unordered daily construction photologs and Building Information Models (BIMs) . First, we describe an automated approach to photo-realistically reconstruct the as-built in 4D and label scene for occupancy. The IFC-based BIM is subsequently fused into the as-built scene by a registration-step and is traversed and labeled for visibility. Finally a machine learning scheme built upon a Bayesian probabilistic model is introduced that automatically detects physical progress in presence of occlusions and demonstrates that progress tracking at schedule activity-level could be fully automated. We present results on automated tracking, analysis and visualization of two ongoing building projects.