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P. Hajela*, B. Fu* and L. Berke+

*Rensselaer Polytechnic Institute, Troy, New York
+Structural Mechanics Branch, NASA Lewis Research Center, Cleveland, Ohio, USA

doi:10.4203/ccp.13.11.1

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P. Hajela, B. Fu, L. Berke, "ART Networks in Automated Conceptual Design of Structural Systems", in B.H.V. Topping, (Editor), "Artificial Intelligence and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 263-271, 1991. doi:10.4203/ccp.13.11.1

Abstract

The present paper examines applications of adaptive resonance theory (ART) based neural networks in structural design. ART networks are vector classifiers, and their proposed use in the present work is to classify given conceptual design situations into one of several categories for which solutions are known. Once such a category is established, the actual generation of design is relegated to a procedural process associated with the category. In the present work, this approach is applied to the design of beam and frame structures for minimum weight and for maximum load carrying capacity. For such problems, the significant issues are shown to reside in selection of problem features used in the model abstraction, and in the choice of network parameters during training and classification.

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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: 13 ARTIFICIAL INTELLIGENCE AND STRUCTURAL ENGINEERING Edited by: B.H.V. Topping Paper XI.1 ART Networks in Automated Conceptual Design of Structural Systems P. Hajela, B. Fu and L. Berke+ *Rensselaer Polytechnic Institute, Troy, New York +Structural Mechanics Branch, NASA Lewis Research Center, Cleveland, Ohio, USA doi:10.4203/ccp.13.11.1 purchase the full-text of this paper Full Bibliographic Reference for this paper P. Hajela, B. Fu, L. Berke, "ART Networks in Automated Conceptual Design of Structural Systems", in B.H.V. Topping, (Editor), "Artificial Intelligence and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 263-271, 1991. doi:10.4203/ccp.13.11.1 Abstract The present paper examines applications of adaptive resonance theory (ART) based neural networks in structural design. ART networks are vector classifiers, and their proposed use in the present work is to classify given conceptual design situations into one of several categories for which solutions are known. Once such a category is established, the actual generation of design is relegated to a procedural process associated with the category. In the present work, this approach is applied to the design of beam and frame structures for minimum weight and for maximum load carrying capacity. For such problems, the significant issues are shown to reside in selection of problem features used in the model abstraction, and in the choice of network parameters during training and classification. 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 £60 +P&P)
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