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Conference Paper: A Smart Construction Object (SCO)-Enabled Proactive Data Management System for Construction Equipment Management

TitleA Smart Construction Object (SCO)-Enabled Proactive Data Management System for Construction Equipment Management
Authors
Issue Date2017
PublisherAmerican Society of Civil Engineers.
Citation
Selected papers from the ASCE International Workshop on Computing in Civil Engineering 2017, Seattle, Washington, 25-27 June 2017. In Lin, KY ... (et al) (eds.), Computing in Civil Engineering 2017: Sensing, Simulation, and Visualization, p. 130-138 How to Cite?
AbstractThe importance of efficient construction equipment management can never be over emphasized. However, in current practices, construction equipment management is reportedly inefficient. It is largely based on rule-of-thumb rather than scientific evidence derived from solid data analytics. It faces challenges such as ‘small’, erratic data. Building on previous studies on smart construction objects (SCOs), this paper aims to develop a SCO-enabled proactive big data management system to facilitate the data collection, data visualization and data analysis for construction equipment management. It does so by first analyzing the problems of data management in prevailing construction equipment management practices. Then, the architecture of the proposed system is presented, which consists of two key components. The first one is a highly customizable smart chip that integrates various sensing and communication modules for proactively collecting and exchanging big data with volume, velocity, and verity inflowing from daily equipment operations. The second one is the data analytics platform for data storage, visualization and analytics. The pilot of the system has just been started in a central police station renovation project in Hong Kong. Fruitful datasets and analytics are expected from the pilot application of the proposed system. It is envisaged that the proposed system can supplement the existing construction equipment management with more comprehensive and concurrent decision-making information.
Persistent Identifierhttp://hdl.handle.net/10722/242846
ISBN

 

DC FieldValueLanguage
dc.contributor.authorNiu, Y-
dc.contributor.authorLu, W-
dc.contributor.authorLiu, D-
dc.contributor.authorChen, K-
dc.contributor.authorXue, F-
dc.date.accessioned2017-08-25T02:46:11Z-
dc.date.available2017-08-25T02:46:11Z-
dc.date.issued2017-
dc.identifier.citationSelected papers from the ASCE International Workshop on Computing in Civil Engineering 2017, Seattle, Washington, 25-27 June 2017. In Lin, KY ... (et al) (eds.), Computing in Civil Engineering 2017: Sensing, Simulation, and Visualization, p. 130-138-
dc.identifier.isbn9780784480830-
dc.identifier.urihttp://hdl.handle.net/10722/242846-
dc.description.abstractThe importance of efficient construction equipment management can never be over emphasized. However, in current practices, construction equipment management is reportedly inefficient. It is largely based on rule-of-thumb rather than scientific evidence derived from solid data analytics. It faces challenges such as ‘small’, erratic data. Building on previous studies on smart construction objects (SCOs), this paper aims to develop a SCO-enabled proactive big data management system to facilitate the data collection, data visualization and data analysis for construction equipment management. It does so by first analyzing the problems of data management in prevailing construction equipment management practices. Then, the architecture of the proposed system is presented, which consists of two key components. The first one is a highly customizable smart chip that integrates various sensing and communication modules for proactively collecting and exchanging big data with volume, velocity, and verity inflowing from daily equipment operations. The second one is the data analytics platform for data storage, visualization and analytics. The pilot of the system has just been started in a central police station renovation project in Hong Kong. Fruitful datasets and analytics are expected from the pilot application of the proposed system. It is envisaged that the proposed system can supplement the existing construction equipment management with more comprehensive and concurrent decision-making information.-
dc.languageeng-
dc.publisherAmerican Society of Civil Engineers.-
dc.relation.ispartofComputing in Civil Engineering 2017: Sensing, Simulation, and Visualization-
dc.rightsComputing in Civil Engineering 2017: Sensing, Simulation, and Visualization. Copyright © American Society of Civil Engineers.-
dc.titleA Smart Construction Object (SCO)-Enabled Proactive Data Management System for Construction Equipment Management-
dc.typeConference_Paper-
dc.identifier.emailLu, W: wilsonlu@hku.hk-
dc.identifier.emailXue, F: xuef@hku.hk-
dc.identifier.authorityLu, W=rp01362-
dc.identifier.authorityXue, F=rp02189-
dc.identifier.doi10.1061/9780784480830.017-
dc.identifier.hkuros274399-
dc.identifier.spage130-
dc.identifier.epage138-
dc.publisher.placeReston, VA-

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