Toward Automated Earned Value Tracking Using 3D Imaging Tools

AbstractAccurate and frequent construction progress tracking provides critical input data for project systems such as cost, schedule control, and billing. Unfortunately, conventional progress tracking is labor intensive, sometimes subject to negotiation, and often driven by arcane rules. Attempts to improve progress tracking have recently focused on automation, using technologies such as three-dimensional imaging, global positioning systems, ultra wide band (UWB) indoor locating, handheld computers, voice recognition, wireless networks, and other technologies in various combinations. However, one limit of these approaches is their focus on counting objects or milestones rather than value. In this paper, a four-dimensional model recognition-driven automated progress tracking system that transforms objects to their earned values is examined via the analysis of data from the construction of a steel reinforced concrete structure and a steel structure. It is concluded that automated, object oriented recognitio...

[1]  Chris Hendrickson,et al.  Project Management for Construction: Fundamental Concepts for Owners, Engineers, Architects, and Builders , 1989 .

[2]  Frédéric Bosché,et al.  Automated progress tracking using 4D schedule and 3D sensing technologies , 2012 .

[3]  Mike Kagioglou,et al.  Automating progress measurement of construction projects , 2009 .

[4]  Burcu Akinci,et al.  Automatic Creation of Semantically Rich 3D Building Models from Laser Scanner Data , 2013 .

[5]  Frédéric Bosché,et al.  Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction , 2010, Adv. Eng. Informatics.

[6]  Frédéric Bosché,et al.  Plane-based registration of construction laser scans with 3D/4D building models , 2012, Adv. Eng. Informatics.

[7]  Osama Moselhi,et al.  Integrating automated data acquisition technologies for progress reporting of construction projects , 2009 .

[8]  Frédéric Bosché,et al.  Automated retrieval of 3D CAD model objects in construction range images , 2008 .

[9]  Edward J. Jaselskis,et al.  Improving transportation projects using laser scanning , 2005 .

[10]  Mani Golparvar-Fard,et al.  Visualization of construction progress monitoring with 4D simulation model overlaid on time-lapsed photographs , 2009 .

[11]  Seung Heon Han,et al.  Object recognition in construction-site images using 3D CAD-based filtering , 2010 .

[12]  Frédéric Bosché,et al.  Fusing 4D modeling and laser scanning for automated construction progress control , 2010 .

[13]  Frédéric Bosché.  Automated Recognition of 3 D CAD Model Objects and Calculation of As-built Dimensions for Dimensional Compliance Control in Construction , 2009 .

[14]  Soon-Wook Kwon,et al.  Fitting range data to primitives for rapid local 3D modeling using sparse range point clouds , 2004 .

[15]  Silvio Savarese,et al.  Monitoring changes of 3D building elements from unordered photo collections , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[16]  Manolis I. A. Lourakis,et al.  Toward automated generation of parametric BIMs based on hybrid video and laser scanning data , 2010, Adv. Eng. Informatics.

[17]  Jim Sumara,et al.  Earned Value—The Next Generation—A Practical Application for Commercial Projects , 1997 .

[18]  David Arditi,et al.  Photo‐net: an integrated system for controlling construction progress , 2003 .

[19]  Mani Golparvar-Fard,et al.  Automated Model-Based Recognition of Progress Using Daily Construction Photographs and IFC-Based 4D Models , 2010 .

[20]  Naai-Jung Shih,et al.  3D Scan Information Management System for Construction Management , 2006 .

[21]  R. Colwell.  Remote sensing of the environment , 1980, Nature.

[22]  Carlos H. Caldas,et al.  Real-Time Three-Dimensional Occupancy Grid Modeling for the Detection and Tracking of Construction Resources , 2007 .

[23]  X. Zhang,et al.  Towards automated progress assessment of workpackage components in construction projects using computer vision , 2009, Adv. Eng. Informatics.

[24]  Mani Golparvar-Fard,et al.  Monitoring of Construction Performance Using Daily Progress Photograph Logs and 4D As-Planned Models , 2009 .

[25]  Burcu Akinci,et al.  Automatic Creation of Semantically Rich 3D Building Models from Laser Scanner Data , 2011 .

[26]  Geraldine S. Cheok,et al.  Ladars for construction assessment and update , 2000 .

[27]  Burcu Akinci,et al.  Automated Recognition of 3D CAD Objects in Site Laser Scans for Project 3D Status Visualization and Performance Control , 2009 .

[28]  Silvio Savarese,et al.  Application of D4AR - A 4-Dimensional augmented reality model for automating construction progress monitoring data collection, processing and communication , 2009, J. Inf. Technol. Constr..

[29]  Feniosky Peña-Mora,et al.  Visual representation of construction progress monitoring metrics on time-lapse photographs , 2007 .

[30]  Frédéric Bosché,et al.  Automated Progress Tracking of Erection of Concrete Structures , 2010 .

[31]  Ammar Peter Kaka,et al.  Towards automatic assessment of workpackage components in construction projects using computer vision , 2009 .

[32]  Mani Golparvar-Fard,et al.  Automated Progress Monitoring Using Unordered Daily Construction Photographs and IFC-Based Building Information Models , 2015, J. Comput. Civ. Eng..

[33]  Burcu Akinci,et al.  Automatic Reconstruction of As-Built Building Information Models from Laser-Scanned Point Clouds: A Review of Related Techniques | NIST , 2010 .

[34]  Frédéric Bosché,et al.  Performance of automated project progress tracking with 3D Data fusion , 2008 .

[35]  Erez N. Allouche,et al.  PHOTO-NET II: a computer-based monitoring system applied to project management , 2003 .