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• W1567875894 abstract "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 1990s, ANN has been increasingly employed as an effective tool in geotechnical engineering, including: constitutive modelling (Agrawal et al., 1994; Gribb & Gribb, 1994; Penumadu et al., 1994; Ellis et al., 1995; Millar & Calderbank, 1995; Ghaboussi & Sidarta 1998; Zhu et al., 1998; Sidarta & Ghaboussi, 1998; Najjar & Ali, 1999; Penumadu & Zhao, 1999); geo-material properities (Goh, 1995; Ellis et al., 1995; Najjar et al., 1996; Najjar and Basheer, 1996; Romero & Pamukcu, 1996; Ozer et al., 2008; Park et al., 2009; Park & Kim, 2010; Park & Lee, 2010; Bearing capacity of pile (Chan et al., 1995; Goh, 1996; Bea et al., 1999; Goh et al., 2005; Teh et al., 1997; Lee & Lee, 1996; Abu-Kiefa, 1998; Nawari et al., 1999; Das & Basudhar, 2006, Park & Cho, 2010); slope stability (Ni et al., 1995; Neaupane and Achet, 2004; Ferentinou & Sakellariou, 2007; Zhao, 2007; Cho, 2009); liquefaction (Agrawal" @default.
• W1567875894 created "2016-06-24" @default.
• W1567875894 creator A5041877158 @default.
• W1567875894 date "2011-04-11" @default.
• W1567875894 modified "2023-10-02" @default.
• W1567875894 title "Study for Application of Artificial Neural Networks in Geotechnical Problems" @default.
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• W1567875894 doi "https://doi.org/10.5772/15011" @default.
• W1567875894 hasPublicationYear "2011" @default.
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