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A.T.C. Goh

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

doi:10.4203/ccp.34.6.2

A.T.C. Goh, "Evaluation of Seismic Liquefaction 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 121-125, 1995. doi:10.4203/ccp.34.6.2

Abstract

Empirical design solutions are commonly used to analyse many geotechnical problems. This paper illustrates through the practical example of the prediction of seismic liquefaction, the potential of neural networks to synthesise data for the development of empirical design aids. A back-propagation neural network was used, and training was carried out using actual field records. The proposed computer model provides rapid results, is more reliable than the more conventional method and can be continually retrained as additional data is acquired.

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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 VI.2 Evaluation of Seismic Liquefaction using Neural Networks A.T.C. Goh School of Civil and Structural Engineering, Nanyang Technological University, Singapore doi:10.4203/ccp.34.6.2 purchase the full-text of this paper Full Bibliographic Reference for this paper A.T.C. Goh, "Evaluation of Seismic Liquefaction 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 121-125, 1995. doi:10.4203/ccp.34.6.2 Abstract Empirical design solutions are commonly used to analyse many geotechnical problems. This paper illustrates through the practical example of the prediction of seismic liquefaction, the potential of neural networks to synthesise data for the development of empirical design aids. A back-propagation neural network was used, and training was carried out using actual field records. The proposed computer model provides rapid results, is more reliable than the more conventional method and can be continually retrained as additional data is acquired. 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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