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R. Liang, N.O. Nawari, J. Nusairat, "Neural Network Models for the Design of Pile Foundations", in B. Kumar, B.H.V. Topping, (Editors), "Artificial Intelligence Applications in Civil and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 117-124, 1999. doi:10.4203/ccp.62.5.2

Abstract

In this paper, neural networks paradigms are implemented to predict the behaviour of axially and laterally loaded piles, using data from full-scale drilled shaft and driven pile tests as well as from published data.

The main objective is to develop optimal models using only simple input data. These data include SPT-N values and the geometrical properties. Neural network models are developed for steel H-piles, steel pipe piles, and pre-stressed and reinforced concrete piles. The models involved are Backpropagation, and Generalised Regression Neural Networks. Simulation results and comparison with the commonly used design methods are presented. Advantages and limitations of using neural networks in the design of pile foundations have been addressed.

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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 V.2 Neural Network Models for the Design of Pile Foundations R. Liang, N.O. Nawari and J. Nusairat Department of Civil Engineering, University of Akron, United States of America doi:10.4203/ccp.62.5.2 purchase the full-text of this paper Full Bibliographic Reference for this paper R. Liang, N.O. Nawari, J. Nusairat, "Neural Network Models for the Design of Pile Foundations", in B. Kumar, B.H.V. Topping, (Editors), "Artificial Intelligence Applications in Civil and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 117-124, 1999. doi:10.4203/ccp.62.5.2 Abstract In this paper, neural networks paradigms are implemented to predict the behaviour of axially and laterally loaded piles, using data from full-scale drilled shaft and driven pile tests as well as from published data. The main objective is to develop optimal models using only simple input data. These data include SPT-N values and the geometrical properties. Neural network models are developed for steel H-piles, steel pipe piles, and pre-stressed and reinforced concrete piles. The models involved are Backpropagation, and Generalised Regression Neural Networks. Simulation results and comparison with the commonly used design methods are presented. Advantages and limitations of using neural networks in the design of pile foundations have been addressed. 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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