Geometry- and Appearance-Based Reasoning of Construction Progress Monitoring
Publication: Journal of Construction Engineering and Management
Volume 144, Issue 2
Abstract
Although adherence to project schedules and budgets is most highly valued by project owners, more than 53% of typical construction projects are behind schedule and more than 66% suffer from cost overruns, partly because of an inability to accurately capture construction progress. To address these challenges, this paper presents new geometry- and appearance-based reasoning methods for detecting construction progress, which has the potential to provide more frequent progress measures using visual data that are already being collected by general contractors. The initial step of geometry-based filtering detects the state of construction of building information modeling (BIM) elements (e.g., in-progress, completed). The next step of appearance-based reasoning captures operation-level activities by recognizing different material types. Two methods have been investigated for the latter step: a texture-based reasoning for image-based 3D point clouds and color-based reasoning for laser-scanned point clouds. This paper presents two case studies for each reasoning approach to validate the proposed methods. The results demonstrate the effectiveness and practical significances of the proposed methods.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request. Information about the Journal’s data sharing policy can be found here: http://ascelibrary.org/doi/10.1061/%28ASCE%29CO.1943-7862.0001263.
Acknowledgments
The authors would like to thank industry partners for providing access to their job sites and all undergraduate students who were involved in web development and data collection. This work is funded in part by the National Science Foundation (NSF) grant CMMI-1360562 and CMMI-1446765, the Department of Defense (DoD) National Defense Science and Engineering Graduate Fellowship (NDSEG), and the National Center for Supercomputing Applications’s Institute for Advanced Computing Applications and Technologies Fellows program. The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPUs used for this research. Any opinions, findings, conclusions, or recommendations presented in this paper are those of the authors and do not reflect the views of NSF, DoD, NCSA, NVIDIA, or the individuals acknowledged above.
References
Alarcón, L. F., Diethelm, S., Rojo, O., and Calderón, R. (2008). “Assessing the impacts of implementing lean construction.” Revista Ingeniería de Construcción, 23(1), 26–33.
Alberts, D. S., and Hayes, R. E. (2005). “Power to the edge eBook.” ⟨http://www.dodccrp.org/files/Alberts_Power.pdf⟩ (Nov. 6, 2017).
Bae, H., Golparvar-Fard, M., and White, J. (2012). “Enhanced (hybrid 4-dimensional augmented reality) for ubiquitous context-aware AEC/FM applications.” Proc., 12th Int. Conf. on Construction Applications of Virtual Reality (CONVR 2012), National Taiwan University Press, Taipei, Taiwan, 253–262.
Ballard, G., and Tommelein, I. (2012). “Lean management methods for complex projects.” Eng. Project Org. J., 2(1–2), 85–96.
Bevan, M., and Steve, J. (2016). “LCI National Webinar: Preliminary findings from national project performance research.” ⟨http://www.leanconstruction.org/media/img/safetyweek/FINAL-2016-05-26_LCI_National_Webinar-Owner_Project_Performance_Study_Overview_and_CallToAction.pdf⟩ (Nov. 6, 2017).
Bosché, F. (2012). “Plane-based registration of construction laser scans with 3D/4D building models.” Adv. Eng. Inf., 26(1), 90–102.
Bosché, F., Guillemet, A., Turkan, Y., Haas, C., and Haas, R. (2014). “Tracking the built status of MEP works: Assessing the value of a scan-vs-BIM system.” J. Comput. Civ. Eng., 05014004.
Brodetskaia, I., Sacks, R., and Shapira, A. (2013). “Stabilizing production flow of interior and finishing works with reentrant flow in building construction.” J. Constr. Eng. Manage., 665–674.
Changal, S., Mohammad, A., and van Nieuwland, M. (2015). “The construction productivity imperative: How to build megaprojects better.” McKinsey & Company, New York.
Chen, Y., and Kamara, J. (2011). “A framework for using mobile computing for information management on construction sites.” Elsevier J. Autom. Constr., 20(7), 776–788.
CII (Construction Industry Institute), ed. (2010). IR252.2a– Guide to activity analysis, Construction Industry Institute, Austin, TX.
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., and Vedaldi, A. (2014). “Describing textures in the wild.” Proc., IEEE Conf. on Computer Vision and Pattern Recognition, IEEE Computer Society, Washington, DC.
Cimpoi, M., Maji, S., and Vedaldi, A. (2015). “Deep filter banks for texture recognition and segmentation.” Proc., IEEE Conf. on Computer Vision and Pattern Recognition, IEEE Computer Society, Washington, DC.
Dave, B., Kubler, S., Främling, K., and Koskela, L. (2014). “Addressing information flow in lean production management and control in construction.” Proc., 22nd Int. Group for Lean Construction (IGLC22), Oslo, Norway, 581–592.
Degol, J., Golparvar-Fard, M., and Hoiem, D. (2016). “Geometry-informed material recognition.” Proc., IEEE Conf. on Computer Vision and Pattern Recognition, IEEE Computer Society, Washington, DC.
Dimitrov, A., and Golparvar-Fard, M. (2014). “Vision-based material recognition for automated monitoring of construction progress and generating building information modeling from unordered site image collections.” Adv. Eng. Inf., 28(1), 37–49.
Eastman, C., Teicholz, P., Sacks, R., and Liston, K. (2011). BIM handbook: A guide to building information modeling for owners, managers, designers, engineers and contractors, Wiley, Hoboken, NJ.
Garcia-Lopez, N. P., and Fischer, M. (2014). “A system to track work progress at construction jobsites.” Proc., 2014 Industrial and Systems Engineering Research Conf., IISE, Norcross, GA.
Goesele, M., Snavely, N., Curless, B., Hoppe, H., and Seitz, S. M. (2007). “Multi-view stereo for community photo collections.” 2007 IEEE 11th Int. Conf. on Computer Vision, IEEE Computer Society, Washington, DC, 1–8.
Golparvar-Fard, M., Bohn, J., Teizer, J., Savarese, S., and Peña Mora, F. (2011). “Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques.” Autom. Constr., 20(8), 1143–1155.
Golparvar-Fard, M., Heydarian, A., and Niebles, J. C. (2013). “Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers.” Adv. Eng. Inf., 27(4), 652–663.
Golparvar-Fard, M., Peña Mora, F., and Savarese, S. (2009). “Application of D4AR: A 4-dimensional augmented reality model for automating construction progress monitoring data collection, processing and communication.” ITcon, 14, 129–153.
Ham, Y., Han, K., Lin, J., and Golparvar-Fard, M. (2016). “Visual monitoring of civil infrastructure systems via camera-equipped unmanned aerial vehicles (UAVs): A review of related works.” Visual. Eng., 4(1), 1–8.
Hamzeh, F., and Bergstrom, E. (2010). “The lean transformation: A framework for successful implementation of the last planner system in construction.” Int. Proc., 46th Annual Conf., Associated Schools of Construction, Fort Collins, CO.
Han, K., and Golparvar-Fard, M. (2015). “Appearance-based material classification for monitoring of operation-level construction progress using 4D BIM and site photologs.” Autom. Constr., 53(0), 44–57.
Han, K., and Golparvar-Fard, M. (2017). “Potential of big visual data and building information modeling for construction performance analytics: An exploratory study.” Autom. Constr., 73, 184–198.
Hayman, E., Caputo, B., Fritz, M., and Eklundh, J.-O. (2004). “On the significance of real-world conditions for material classification.” European Conf. on Computer Vision, Springer, Berlin.
Horn, B. K. P. (1987). “Closed-form solution of absolute orientation using unit quaternions.” J. Opt. Soc. Am. A, 4(4), 629–642.
Kamat, V., and Akula, M. (2011). “Integration of global positioning system and inertial navigation for ubiquitous context-aware engineering applications.” Proc., National Science Foundation Grantee Conf., National Science Foundation, Alexandria, VA, 1–10.
Kim, C., Son, H., and Kim, C. (2013). “Automated construction progress measurement using a 4D building information model and 3D data.” Autom. Constr., 31(0), 75–82.
Kim, Y., and Ballard, G. (2010). “Management thinking in the earned value method system and the last planner system.” J. Manage. Eng., 223–228.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). “Imagenet classification with deep convolutional neural networks.” Adv. Neural Inf. Proc. Syst., 25, 1097–1105.
Li, S., Becerik-Gerber, B., Jog, G., and Brilakis, I. (2011). “Civil and environmental engineering challenges for data sensing and analysis.” Computing in civil engineering, ASCE, Reston, VA, 110–117.
Lowe, D. (1999). “Object recognition from local scale-invariant features.” Int. Conf. on Computer Vision, IEEE Computer Society, Washington, DC.
NIST. (2011). “2011-2012 criteria for performance excellence.” Gaithersburg, MD.
NRC (National Research Council). (2009). Advancing the competitiveness and efficiency of the U.S. construction industry, National Academies Press, Washington, DC.
O’Brien, W. J., Formoso, C. T., Ruben, V., and London, K. (2008). Construction supply chain management handbook, CRC Press, Boca Raton, FL.
O’Malley, S. (2015). “Getting wired: Contractors find value in on-the-job tech.” ⟨https://www.constructiondive.com/news/getting-wired-contractors-find-value-in-on-the-job-tech/392583/⟩ (Nov. 7, 2017).
Perronnin, F., Sánchez, J., and Mensink, T. (2010). “Improving the Fisher kernel for large-scale image classification.” European Conf. on Computer Vision, Springer, Berlin.
Sacks, R., Barak, R., Belaciano, B., Gurevich, U., and Pikas, E. (2013). “KanBIM workflow management system: Prototype implementation and field testing.” Lean Constr. J., 9(1), 19–34.
Sacks, R., Koskela, L., Dave, B., and Owen, R. (2010a). “Interaction of lean and building information modeling in construction.” J. Constr. Eng. Manage., 968–980.
Sacks, R., Radosavljevic, M., and Barak, R. (2010b). “Requirements for building information modeling based lean production management systems for construction.” Autom. Constr., 19(5), 641–655.
Salem, O., Solomon, J., Genaidy, A., and Luegring, M. (2005). “Site implementation and assessment of lean construction techniques.” Lean Constr. J., 2(2), 1–21.
Siebert, S., and Teizer, J. (2014). “Mobile 3D mapping for surveying earthwork projects using an unmanned aerial vehicle (UAV) system.” Autom. Constr., 41, 1–14.
Turkan, Y., Bosché, F., Haas, C., and Haas, R. (2012). “Automated progress tracking using 4D schedule and 3D sensing technologies.” Autom. Constr., 22(0), 414–421.
Turkan, Y., Bosché, F., Haas, C., and Haas, R. (2013). “Toward automated earned value tracking using 3D imaging tools.” J. Constr. Eng. Manage, 139(4), 423–433.
Wu, C. (2016). “VisualSFM: A visual structure from motion system.” ⟨http://ccwu.me/vsfm/⟩ (Nov. 7, 2017).
Young, N., Jones, S., Bernstein, H. M., and Gudgel, J. (2009). The business value of BIM-getting building information modeling to the bottom line, McGraw-Hill Construction, Bedford, MA.
Information & Authors
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Published In
Journal of Construction Engineering and Management
Volume 144 • Issue 2 • February 2018
Copyright
©2017 American Society of Civil Engineers.
History
Received: Apr 13, 2017
Accepted: Aug 1, 2017
Published online: Nov 30, 2017
Published in print: Feb 1, 2018
Discussion open until: Apr 30, 2018
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