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Paper w78-1998-34:
IT support for the very early design of buildings and civil engineering works

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Ozsariyildiz S, Tolman F

IT support for the very early design of buildings and civil engineering works

Abstract: Despite a general agreement about the importance of very early design decisions (various sources estimate that between 60% to 80% of the total project costs are determined during this stage), the very early design stage of building and civil engineering projects is not yet adequately supported by IT. The paper focusses on the problems that are causing the lack of IT support and reports on a possible solution based on the application of Product Data Technology (PDT) and Knowledge Engineering. The paper will show some initial experience with the development and application of an Inception Modeller that implements ideas from the General AEC Reference Model (GARM) as proposed by Wim Gieling in 1988. The development takes place in co-operation with the Brite-Euram CONCUR-project. The system concentrates on the inception and very early design of technical buildings, i.e. buildings in which equipment plays a major role, like power plant buildings, hospitals, factories, etc. The basic idea is to support the choice and elaboration of Technical Solutions that fulfil the requirements of Functional Units. The knowledge base is structured according to a FU-TS decomposition, or Hamburger model, of the building. A knowledge acquisition tool based on the same Hamburger model is under development and will be explained in some detail in the final paper. The system is implemented in Java, using Clips as the knowledge engine and VRML for the visualization. Though it is probably still too early to draw any definitive conclusions, it looks as if the structure provided by the FU-TS decomposition is ideal for very early design support. It provides a means to capture and re-use knowledge of successful earlier designs, thus providing a mechanism still missing in the building and construction industry.



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Series: w78:1998 (browse)
Cluster: papers of the same cluster (result of machine made clusters)
Class: class.bestPractise (0.007643) class.deployment (0.005435) class.education (0.005124)
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Permission to reproduce these papers has been graciously provided by Royal Institute of Technology, Stockholm, Sweden. The assistance of the editors, Prof. Bo-Christer Björk and Dr. Adina Jägbeck, is gratefully appreciated.


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