CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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authors Naticchia, Berardo
year 1999
title Physical Knowledge in Patterns: Bayesian Network Models for Preliminary Design
source Architectural Computing from Turing to 2000 [eCAADe Conference Proceedings / ISBN 0-9523687-5-7] Liverpool (UK) 15-17 September 1999, pp. 611-619
summary Computer applications in design have pursued two main development directions: analytical modelling and information technology. The former line has produced a large number of tools for reality simulation (i.e. finite element models), the latter is producing an equally large amount of advances in conceptual design support (i.e. artificial intelligence tools). Nevertheless we can trace rare interactions between computation models related to those different approaches. This lack of integration is the main reason of the difficulty of CAAD application to the preliminary stage of design, where logical and quantitative reasoning are closely related in a process that we often call 'qualitative evaluation'. This paper briefly surveys the current development of qualitative physical models applied in design and propose a general approach for modelling physical behaviour by means of Bayesian network we are employing to develop a tutoring and coaching system for natural ventilation preliminary design of halls, called VENTPad. This tool explores the possibility of modelling the causal mechanism that operate in real systems in order to allow a number of integrated logical and quantitative inference about the fluid-dynamic behaviour of an hall. This application could be an interesting connection tool between logical and analytical procedures in preliminary design aiding, able to help students or unskilled architects, both to guide them through the analysis process of numerical data (i.e. obtained with sophisticate Computational Fluid Dynamics software) or experimental data (i.e. obtained with laboratory test models) and to suggest improvements to the design.
keywords Qualitative Physical Modelling, Preliminary Design, Bayesian Networks
series eCAADe
full text file.pdf (1,647,218 bytes)
references Content-type: text/plain
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