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NIST Authors in Bold
|Author(s):||Kamel S. Saidi; Geraldine S. Cheok; Marek Franaszek; Christopher U. Brown; Jeremy Swerdlow; Robert R. Lipman; Itai Katz; Mani Golparvar-Fard; Paul Goodrum; Manu Akula; Gabriel Dadi; Behshad Ghadimi;|
|Title:||Development and Use of the NIST Intelligent and Automated Construction Job Site Testbed|
|Published:||December 16, 2011|
|Abstract:||The Intelligent and Automated Construction Job Site (IACJS) Testbed is a large laboratory facility that was established at the National Institute of Standards and Technology (NIST) in 2011. The creation of the testbed was motivated by the need for such testbeds that was identified through several industry initiatives. The capabilities of the testbed include various equipment (such as mobile and cable-driven robots), sensors (such as laser scanners and laser trackers), and computation and visualization resources (such as high-end workstations and high-resolution projectors and cameras). The application of 3D imaging technologies to six construction industry scenarios, identified in collaboration with industry partners, was selected for potential demonstration in the testbed. The scenario, rebar mapping for cast-in-place concrete railway bridge decks, was selected as the initial construction activity to which 3D imaging would be applied. An industry demonstration was conducted in the testbed on June 30, 2011. Two 3D imaging technologies, laser scanning and digital images from a camera using custom software to obtain 3D data, were evaluated based on how well they performed and how they affected productivity. The productivity analysis was conducted using Discrete Event Simulation techniques and the ASTM E2204 Standard Guide for Summarizing the Economic Impacts of Building Related Projects (ASTM, 2011). This report describes the IACJS, the industry demonstration, and results from the experiments conducted during the demonstration.|
|Citation:||Technical Note (NIST TN) - 1726|
|Research Areas:||Intelligent/Automated Construction|
|PDF version:||Click here to retrieve PDF version of paper (7MB)|