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ARTIFICIAL NEURAL NETWORKS METHODOLOGICAL ADVANCES AND BIOMEDICAL APPLICATIONS_2

Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications in various areas. The purpose of this book is to provide recent advances of artificial neural networks in biomedical applications. The book begins with fundamentals of artificial neural networks, which cover an introduction, design, and optimization. | Part 4 Application of ANN in Engineering 15 Study for Application of Artificial Neural Networks in Geotechnical Problems Hyun II Park Samsung C T Korea of Republic 1. Introduction The geotechnical engineering properties of soil exhibit varied and uncertain behaviour due to the complex and imprecise physical processes associated with the formation of these materials Jaksa 1995 . This is in contrast to most other civil engineering materials such as steel concrete and timber which exhibit far greater homogeneity and isotropy. In order to cope with the complexity of geotechnical behaviour and the spatial variability of these materials traditional forms of engineering design models are justifiably simplified. Moreover geotechnical engineers face a great amount of uncertainties. Some sources of uncertainty are inherent soil variability loading effects time effects construction effects human error and errors in soil boring sampling in-situ and laboratory testing and characterization of the shear strength and stiffness of soils. Although developing an analytical or empirical model is feasible in some simplified situations most manufacturing processes are complex and therefore models that are less general more practical and less expensive than the analytical models are of interest. An important advantage of using Artificial Neural Network ANN over regression in process modeling is its capacity in dealing with multiple outputs or responses while each regression model is able to deal with only one response. Another major advantage for developing NN process models is that they do not depend on simplified assumptions such as linear behavior or production heuristics. Neural networks possess a number of attractive properties for modeling a complex mechanical behavior or a system universal function approximation capability resistance to noisy or missing data accommodation of multiple nonlinear variables for unknown interactions and good generalization capability. Since the early .