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J.D. Biedermann and D.E. Grierson

Department of Civil Engineering, University of Waterloo, Waterloo, Canada

doi:10.4203/ccp.27.2.2

J.D. Biedermann, D.E. Grierson, "Representing Heuristic Design Knowledge using Artificial Neural Networks", in B.H.V. Topping, M. Papadrakakis, (Editors), "Artificial Intelligence and Object Oriented Approaches to Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 83-90, 1994. doi:10.4203/ccp.27.2.2

Abstract

The detailed stage of structural design has benefited considerably from computer automation of the numerically intensive tasks of structural analysis, optimization and conformance checking using the procedural programming approach. Such an approach, however, does not allow for the representation and utilization of heuristic knowledge implicit in previous design solutions, which is often difficult or impossible to represent algorithmically. This paper describes how artificial neural networks can learn heuristic knowledge from previous design solutions and then apply this knowledge to produce a solution to a similar design problem.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 27 ARTIFICIAL INTELLIGENCE AND OBJECT ORIENTED APPROACHES TO STRUCTURAL ENGINEERING Edited by: B.H.V. Topping and M. Papadrakakis Paper II.2 Representing Heuristic Design Knowledge using Artificial Neural Networks J.D. Biedermann and D.E. Grierson Department of Civil Engineering, University of Waterloo, Waterloo, Canada doi:10.4203/ccp.27.2.2 purchase the full-text of this paper Full Bibliographic Reference for this paper J.D. Biedermann, D.E. Grierson, "Representing Heuristic Design Knowledge using Artificial Neural Networks", in B.H.V. Topping, M. Papadrakakis, (Editors), "Artificial Intelligence and Object Oriented Approaches to Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 83-90, 1994. doi:10.4203/ccp.27.2.2 Abstract The detailed stage of structural design has benefited considerably from computer automation of the numerically intensive tasks of structural analysis, optimization and conformance checking using the procedural programming approach. Such an approach, however, does not allow for the representation and utilization of heuristic knowledge implicit in previous design solutions, which is often difficult or impossible to represent algorithmically. This paper describes how artificial neural networks can learn heuristic knowledge from previous design solutions and then apply this knowledge to produce a solution to a similar design problem. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £45 +P&P)
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