NDLI: Automated Construction Resource Location Tracking to Support the Analysis of Lean Principles

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Automated Construction Resource Location Tracking to Support the Analysis of Lean Principles

Content Provider	Semantic Scholar
Author	Cheng, Tao Yang, Jun Teizer, Jochen Vela, Patricio A.
Copyright Year	2010
Abstract	This paper presents a research framework and preliminary experimental results to automated construction resource (workforce, equipment, materials) location tracking for the purpose of advanced lean planning and rapid decision making. Based on the statement “what can be measured, can also be changed”, the research hypothesis was formulated that advanced automated remote sensing technology can measure and improve work site performance and assist decision making. The initial research scope focused on testing emerging real-time location tracking and data analysis technology (Ultra Wideband and Video) applied in capital intensive construction site settings. A literature review is presented on existing observation techniques that have been used in the analysis of lean construction operations. The research framework and technology in context to lean construction is explained next. To better understand construction operations – and in particular construction site activities related to safety and productivity – location and movements of workers, equipment, and materials were recorded in real-time. Preliminary results to field experiments demonstrate the feasibility of tracking construction resources accurately and in real-time. An outlook and applications are presented of how the collected resource trajectory information can be used in project decision making. It is envisioned, that once site resource data is collected, processed, and linked to existing schedule and work task planning, the information can play a vital role for rapid implementation of lean principles in the operational environment of construction sites.
Starting Page	643
Ending Page	653
Page Count	11
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://iglcstorage.blob.core.windows.net/papers/attachment-7549b1fe-9ee1-40d6-a8d9-3fe9e4427517.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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