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A. Kaveh, H. Servati, "Analysis and Design of Double Layer Grids using Neural Networks", in B. Kumar, B.H.V. Topping, (Editors), "Artificial Intelligence Applications in Civil and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 93-101, 1999. doi:10.4203/ccp.62.4.2

Keywords: double layer grids, neural networks, backpropagation, maximum deflection, weight, design, data ordering..

Abstract

In this paper efficient neural networks are trained for design of double layer grids. Square oblique-on-oblique grids with spans varying between 25 and 75 meters are considered. Backpropagation algorithm is employed for training efficient neural networks for evaluation of the maximum deflection, weight, and design of double layer grids. A special method is developed for data ordering to reduce the nonlinearity of the data and to increase the speed of training. This approach also provides the necessary stability. Additional neural nets are trained and tested for design of grids using the developed data ordering.

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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: 62 ARTIFICIAL INTELLIGENCE APPLICATIONS IN CIVIL AND STRUCTURAL ENGINEERING Edited by: B. Kumar and B.H.V. Topping Paper IV.2 Analysis and Design of Double Layer Grids using Neural Networks A. Kaveh and H. Servati Iran University of Science and Technology, Tehran, Iran doi:10.4203/ccp.62.4.2 purchase the full-text of this paper Full Bibliographic Reference for this paper A. Kaveh, H. Servati, "Analysis and Design of Double Layer Grids using Neural Networks", in B. Kumar, B.H.V. Topping, (Editors), "Artificial Intelligence Applications in Civil and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 93-101, 1999. doi:10.4203/ccp.62.4.2 Keywords: double layer grids, neural networks, backpropagation, maximum deflection, weight, design, data ordering.. Abstract In this paper efficient neural networks are trained for design of double layer grids. Square oblique-on-oblique grids with spans varying between 25 and 75 meters are considered. Backpropagation algorithm is employed for training efficient neural networks for evaluation of the maximum deflection, weight, and design of double layer grids. A special method is developed for data ordering to reduce the nonlinearity of the data and to increase the speed of training. This approach also provides the necessary stability. Additional neural nets are trained and tested for design of grids using the developed data ordering. 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 £50 +P&P)
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