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Computational fieldwork support for ...

Carnegie Mellon University.

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Computational fieldwork support for efficient Operation and Maintenance of mechanical, electrical and plumbing systems.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Computational fieldwork support for efficient Operation and Maintenance of mechanical, electrical and plumbing systems./
作者:	Lee, Sang Hoon.
面頁冊數:	341 p.
附註:	Adviser: Omer Akin.
Contained By:	Dissertation Abstracts International70-03A.
標題:	Architecture. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3350503
ISBN:	9781109054477

Computational fieldwork support for efficient Operation and Maintenance of mechanical, electrical and plumbing systems.
Lee, Sang Hoon.

Computational fieldwork support for efficient Operation and Maintenance of mechanical, electrical and plumbing systems. - 341 p.

Adviser: Omer Akin.

Thesis (Ph.D.)--Carnegie Mellon University, 2009.

There is significant potential for improvement in the performance of Operation and Maintenance (O&M) fieldwork. O&M occurs throughout the lifecycle of a building; the majority of expenses in a building's lifecycle are incurred during O&M. Many strategies have been developed to enhance the O&M environment. However, it is well-known that the maintenance industry adapts new technologies more slowly than other industries. Although the industry's O&M support systems have been enhanced considerably, its overall style of O&M fieldwork has remained essentially unchanged for decades. Furthermore, tradespeople, whose primary roles are O&M fieldwork, vastly underutilize information in the field due to problems with information accessibility and reliability.

ISBN: 9781109054477Subjects--Topical Terms:

523581
Architecture.

Computational fieldwork support for efficient Operation and Maintenance of mechanical, electrical and plumbing systems.
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245 1 0 $a Computational fieldwork support for efficient Operation and Maintenance of mechanical, electrical and plumbing systems.
300 $a 341 p.
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500 $a Source: Dissertation Abstracts International, Volume: 70-03, Section: A, page: 0723.
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520 $a There is significant potential for improvement in the performance of Operation and Maintenance (O&M) fieldwork. O&M occurs throughout the lifecycle of a building; the majority of expenses in a building's lifecycle are incurred during O&M. Many strategies have been developed to enhance the O&M environment. However, it is well-known that the maintenance industry adapts new technologies more slowly than other industries. Although the industry's O&M support systems have been enhanced considerably, its overall style of O&M fieldwork has remained essentially unchanged for decades. Furthermore, tradespeople, whose primary roles are O&M fieldwork, vastly underutilize information in the field due to problems with information accessibility and reliability.
520 $a This research investigates current practices from the initial phase of assigning O&M requests through the completion of the requests in order to identify inefficiency in O&M fieldwork and to develop strategies to improve the environment from the perspective of computational support. As the first step, shadowing tradespeople was conducted to better understand current O&M fieldwork and pinpoint bottlenecks in the workflow. Statistical analyses (F-test, Analysis of Variance and R2-Test) were conducted to see the correlation among O&M activities as well as the similarity of the collected data.
520 $a An Augmented Reality (AR)-based Operation and Maintenance Fieldwork Facilitator (AROMA-FF) is developed to computationally support O&M fieldwork. An O&M information model is developed by enhancing an existing Building Information Model with the data collected from O&M fieldwork practice. An Augmented Reality-based interface is developed for an intuitive user interface. BACnet protocol is used to get sensor-derived operation data in real time from Building Automation Systems.
520 $a A series of experiments was conducted in order to quantitatively measure improvement in O&M efficiency by using a software prototype of the AR-based O&M Fieldwork Facilitator. The key metric was time spent on O&M activities. The most impressive finding from the experiment is that while the subjects were trying to locate the target area, they spent, on average, 49% less time with the prototype than conventional strategies in addition to an 8% decrease in time spent getting operation-related data. These results show that the prototype is capable of improving O&M fieldwork efficiency.
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