Abstract
Accurate and efficient tracking, analysis and visualization of as-built (actual) status of buildings under construction 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. This paper presents a new automated approach for recognition of physical progress based on two emerging sources of information: (1) unordered daily construction photo collections, which are currently collected at almost no cost on all construction sites; and (2) building information models (BIMs), which are increasingly turning into binding components of architecture/engineering/construction contracts. First, given a set of unordered and uncalibrated site photographs, an approach based on structure-from-motion, multiview stereo, and voxel coloring and labeling algorithms is presented that calibrates cameras, photorealistically reconstructs a dense as-built point cloud model in four dimensions (three dimensions + time), and traverses and labels the scene for occupancy. This strategy explicitly accounts for occlusions and allows input images to be taken far apart and widely distributed around the environment. An Industry Foundation Class–based (IFC-based) BIM is subsequently fused into the as-built scene by a robust registration step and is traversed and labeled for expected progress visibility. Next, a machine-learning scheme built upon a Bayesian probabilistic model is proposed that automatically detects physical progress in the presence of occlusions and demonstrates that physical progress monitoring at schedule activity level could be fully automated. Finally, the system enables the expected and reconstructed elements to be explored with an interactive, image-based, three-dimensional (3D) viewer where deviations are automatically color-coded over the IFC-based BIM. To that extent, the underlying hypotheses and algorithms for generating integrated four-dimensional (4D) as-built and as-planned models plus automated progress monitoring are presented. Experimental results are reported for challenging image data sets collected under different lighting conditions and severe occlusions from two ongoing building construction projects. This marks the presented model as being the first probabilistic model for automated progress tracking and visualization of deviations that incorporates both as-planned models and unordered daily photographs in a principled way.
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Acknowledgments
Authors like to thank Turner Construction and University of Illinois–Housing and Facilities and Services for their contributions to this research project. Special thanks to Mr. Greg Cuttell, Turner Construction’s project manager and Mr. Robert Bursack and Mr. Nick Canellis, Turner Construction’s project executives. This work was funded by the National Science Foundation grant CMMI-0800500. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not reflect the views of the National Science Foundation or the companies and individuals mentioned above.
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Journal of Computing in Civil Engineering
Volume 29 • Issue 1 • January 2015
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© 2014 American Society of Civil Engineers.
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Received: Dec 21, 2011
Accepted: Feb 9, 2012
Published online: Feb 11, 2012
Discussion open until: Aug 25, 2014
Published in print: Jan 1, 2015
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