| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | August 24, 2010 |
| Latest Amendment Date: | March 27, 2014 |
| Award Number: | 1030472 |
| Award Instrument: | Standard Grant |
| Program Manager: |
elise miller-hooks
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | September 1, 2010 |
| End Date: | August 31, 2015 (Estimated) |
| Total Intended Award Amount: | $299,966.00 |
| Total Awarded Amount to Date: | $299,966.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
926 DALNEY ST NW ATLANTA GA US 30318-6395 (404)894-4819 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
926 DALNEY ST NW ATLANTA GA US 30318-6395 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | CIS-Civil Infrastructure Syst |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
This research seeks to prove that it is possible to reliably and automatically track work progress and multiple resources with images (video and/or time-lapse) in order to reproduce the daily workflow activities associated to a construction worksite. The task of measuring the progress of construction site activities that involve workers, large machines, and materials, has often been a subjective and intensive manual process that is prone to error and, in real operations, frequently out-of-date. Demonstrating that an active vision system can effectively analyze and assess work-site progress will assist project managers by reducing the time spent monitoring and interpreting project status and performance, thus enabling increased attention to control of cost and schedule. By making project management and workforce more aware of the performance status of their project and their work environment, potential savings to the industry are envisioned. The track data will be interpreted and used to provide understanding of the spatio-temporal evolution of a worksite for automatically generating knowledge about worksite operations. In an information-based framework, much effort is spent acquiring and interpreting information. In a knowledge-based framework, efforts are allocated to making decisions based on the interpreted information.
If successful, this research will transform the review and management of construction operations from being information-based to knowledge-based, thus saving human resources and improving decision effectiveness. This research has broader appeal beyond construction. Research domains incorporating or requiring vision-based sensing, diverse resources (people, small to heavy machinery, goods, etc.), and processing of the visual data for awareness of operations and activities are additional investigation domains. Examples include airport ground operations and mining operations. Contributions are also expected in the fields of machine learning and computer vision. The proposed research will impact research into site operations by enabling the automated monitoring and tracking of site resources. Video-based monitoring and processing algorithms provide a non-intrusive, easy, and, rapid mechanism for generating a body of operational information and knowledge which, when made available, will enable inquiry into construction operations that is currently not possible. Longer term, this research will serve as a valuable aid to project management by enabling tighter control and greater efficiency. By making project management and workforce more aware of the performance status of their project and their work environment, potential savings to the construction and other industries are envisioned. This research will also actively include and drive the education of the next generation of engineers (civil, electrical, and computational engineering) and construction labor pool. The research has a dedicated outreach plan to involve in this research a broad spectrum of students from high schools and industry professionals who are interested in advanced hard- and software technology
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The research demonstrated that it is possible to reliably and automatically track work progress and multiple resources with images from video. The track data provides sufficient information to summarize the recorded activities and collect statistics regarding the activities associated to a construction worksite. Verification of this achievement was accomplished by (1) assessing the productivity of an earthwork process associated to excavators and dump trucks and (2) tracking the cyclic work activities of a crane arm. Furthermore, the accuracy of vision-based tracking algorithms was verified and shown to be accurate to less than a meter when tracking personnel and given known ground geometry.
A parallel research thread investigating the feasibility of tag-based monitoring has shown that it is accurate to less than half a meter. The tag-based system was also demonstrated to provide (1) valuable activity state information, (2) sufficient operational data for safety assessment by a proposed safety index, and (3) sufficient operational statistics to feed a cell-based model of worksite operations for predictive analytics (productivity and safety).
Intellectual Merit. These two results are important as they pave the way for more extensive future studies on algorithms for assessing and interpreting construction site operations. When the work began, vision-based sensors were not considered reliable enough, and the installation requirements a detriment to deployment. Today, cameras are increasingly being installed on the worksite for safety and project management purposes. Though the reliability of automated visual processing algorithms is still in question, the accomplishments of this award demonstrate that it is a surmountable issue and provide a framework for accomplishing the automated analysis of monitoring video. The work affirms that an vision system coupled to automated procesing algorithms can effectively analyze and assess work-site progress. It also affirms that such a system will ultimately be capable of assisting project managers by reducing the time spent monitoring and interpreting project status and performance, thus enabling increased attention to control of cost and schedule. To achieve that end, the project has also identified several areas of study that must be resolved for the research to transition.
Broader Impact. By making project management and workforce more aware of the performance status of their project and their work environment, potential savings to the industry are envisioned. The results achieved during this project confirm that sensor-based monitoring of construction sites can transform the review and management of construction operations from being information-based to knowledge-based, thus saving human resources and improving decision effectiveness.
Execution of the proposed work involved several graduate students, a multitude of undergraduate student researchers, and several outreach efforts to local K-12 students. The evolution of the research opportunities led to the creation of a long-term, multi-disciplinary research-based course on sensing and robotics in construction. Outreach to industry has demonstrated that the tag-based system can also improve worker training by providing post-activity analysis of a workers' safety index and effectiveness during the task.
Last Modified: 11/30/2015
Modified by: Patricio A Vela
