1400 North 14th Street, 12th Floor
Arlington, VA 22209
LCI San Diego Community of Practice
Date: Thursday, January 12, 2017
Time: 4:30 p.m. Reception
5:30 p.m. Presentation
7:00 p.m. Adjourn
Achieving smooth flow of production in construction requires team-based planning and systematic avoidance of waste through production control mechanisms. Over the past decade, production control theories such as the Last Planner System have emerged that stabilize workflows by shielding the direct work from upstream variation and uncertainty. While the benefits of these theories are well documented, their potential across the life of a construction project is not fully achieved and the root-causes for this are not entirely understood. A large body of empirical observations suggest that successful implementation of control mechanisms requires dedicated facilitators and engages practitioners in a relatively deep learning process. Sustaining this level of commitment for the duration of a project can be difficult and in its absence, project teams may revert back to traditional project control practices. While these barriers are mostly attributed to the people and organizational processes involved in implementing lean principles, yet there is a growing recognition that the functional aspects of production control techniques need close re-examination to better understand, predict and analyze reliability in performance, and preserve effective and timely flow of information both to and from the workface.
To address these knowledge gaps, this talk presents a new visual production management system that easily and quickly captures, communicates and analyzes actual and potential construction performance problems. To ensure its implementation does not take away from actual productivity, the web-based system extends the value of 4D (3D+time) Building Information Models (BIM) commonly used for constructability review as a benchmark for performance. Likewise, it takes advantage from images and videos frequently collected by project participants or professional services via smartphone and drone cameras to visually document actual performance. These images and videos are used together with a cloud-based computer vision and machine learning platform to continuously map the current state of production in 3D and then 4D. The 4D point clouds and their associated still images and videos are integrated and compared continuously with the 4D BIM Plan to expose actual waste and highlight potential issues by forecasting reliability in project look-ahead schedules. Experimental results from implementing this system on several real-world construction projects will be shared to demonstrate how these visual production models together with actionable data analytics on construction performance can proactively support collaborative decision making that eliminates root causes of waste. The system also provides visual interfaces between people and information that enable effective pull flow, decentralize work tracking and facilitate in-process quality control and hand-overs among contractors.
Dr. Mani Golparvar is Associate Professor of Civil Engineering and of Computer Science, Faculty Entrepreneurial Fellow, and the director of the Real-time and Automated Monitoring and Control (RAAMAC) lab at University of Illinois at Urbana-Champaign (UIUC). He received his PhD degree in Civil Engrg and MS degree in Computer Science from UIUC in 2010, MASc in Civil Engrg from University of British Columbia in 2006, and MS and BS in Civil Engrg from Iran University of Science and Technology in 2005 and 2002 respectively. Prior to joining the faculty at UIUC, he was an Assistant Professor in Civil Engineering at Virginia Tech. He is the recipient of the 2016 ASCE Dan H. Halpin Award for Scholarship in Construction, 2013 ASCE James R. Croes Medal for innovation in Civil Engrg, 2013 FIATECH CETI award for outstanding researcher, 2012 best paper award from the ASCE Journal of Construction Engineering and Management, and numerous conference paper awards. Dr. Golparvar serves on the editorial board of the ASCE Journal of Computing in Civil Engineering, ASCE Journal of Construction Engrg and Management, and Elsevier Journal of Automation in Construction. He is also the founder of RECONSTRUCT, a startup company that offers visual data analytics to construction projects. The underlying technology is licensed from UIUC and is recognized by several awards including an Innovation Award from Turner Construction Company in 2015.
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