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A.T.C. Goh, K.S. Wong and B.B. Broms

School of Civil and Structural Engineering, Nanyang Technological University, Singapore

doi:10.4203/ccp.34.3.1

A.T.C. Goh, K.S. Wong, B.B. Broms, "Multivariate Modelling of FEM Data using Neural Networks", in B.H.V. Topping, (Editor), "Developments in Neural Networks and Evolutionary Computing for Civil and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 59-64, 1995. doi:10.4203/ccp.34.3.1

Abstract

Many civil engineering problems are based on an understanding of relationships between variables. Many of these variables are established from experimental or numerical observations and are defined in terms of algebraic expressions involving the variables. This paper focuses on the potential applications of neural networks for evaluating the relationships between these variables and for modelling complex multivariate systems. Demonstration of the potential of this approach is illustrated through the example of the prediction of wall deflections for braced excavations in clay.

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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: 34 DEVELOPMENTS IN NEURAL NETWORKS AND EVOLUTIONARY COMPUTING FOR CIVIL AND STRUCTURAL ENGINEERING Edited by: B.H.V. Topping Paper III.1 Multivariate Modelling of FEM Data using Neural Networks A.T.C. Goh, K.S. Wong and B.B. Broms School of Civil and Structural Engineering, Nanyang Technological University, Singapore doi:10.4203/ccp.34.3.1 purchase the full-text of this paper Full Bibliographic Reference for this paper A.T.C. Goh, K.S. Wong, B.B. Broms, "Multivariate Modelling of FEM Data using Neural Networks", in B.H.V. Topping, (Editor), "Developments in Neural Networks and Evolutionary Computing for Civil and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 59-64, 1995. doi:10.4203/ccp.34.3.1 Abstract Many civil engineering problems are based on an understanding of relationships between variables. Many of these variables are established from experimental or numerical observations and are defined in terms of algebraic expressions involving the variables. This paper focuses on the potential applications of neural networks for evaluating the relationships between these variables and for modelling complex multivariate systems. Demonstration of the potential of this approach is illustrated through the example of the prediction of wall deflections for braced excavations in clay. 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 £55 +P&P)
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