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Decision support to the application of intelligent building technologies

Authors: Yang, Jay; Peng, Hua;
Abstract
Although considered a current highlight, the concept of intelligent buildings (IB) has not been adopted as quickly and widely as expected. One of the reasons for this is the lack of information and knowledge support to all professionals involved at the design stage of a project. This paper provides a brief overview on the new advances of IB technologies and discusses ways of supplementing the decision making process by adopting two methods for economical and technical aspects of IB applications. It goes on to introduce an on-going research which aims to utilise knowledge based systems to provide comprehensive evaluations to alternatives and decision support to retrofit building projects, when they are being conceptualised.
Country
Australia
Related Organizations
- Queensland University of Technology Australia
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).58 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 1% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average

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citations
Citations provided by BIP!
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
popularity
Popularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
58
Top 10%
Top 1%
Average
bronze
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