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dc.contributor.authorShahin, Mohamed
dc.contributor.editorXin-She Yang
dc.contributor.editorAmir Hossein Gandomi
dc.contributor.editorSiamak Talatahari
dc.contributor.editorAmir Hossein Alavi
dc.identifier.citationShahin, Mohamed A. 2012. Artificial Intelligence in Geotechnical Engineering: Applications, Modeling Aspects, and Future Directions, in Yang, X. and Gandomi, A. and Talatahari, S. and Alavi, A. (ed), Metaheuristics in Water, Geotechnical and Transport Engineering. pp. 169-194. London, UK: Elsevier Science.

Geotechnical engineering deals with materials (e.g., soil and rock) that, by their very nature, exhibit varied and uncertain behavior due to the imprecise physical processes associated with the formation of these materials. Modeling the behavior of such materials is complex and usually beyond the ability of most traditional forms of physically based engineering methods. Artificial intelligence (AI) is becoming more popular and particularly amenable to modeling the complex behavior of most geotechnical engineering materials because it has demonstrated superior predictive ability compared to traditional methods. Over the last decade, AI has been applied successfully to virtually every problem in geotechnical engineering. However, despite this success, AI techniques are still facing classical opposition due to some inherent reasons such as lack of transparency, knowledge extraction, and model uncertainty, which will be discussed in detail in this chapter. Among the available AI techniques are artificial neural networks (ANNs), genetic programming (GP), evolutionary polynomial regression (EPR), support vector machines, M5 model trees, and K-nearest neighbors (Elshorbagy et al.,2010). In this chapter, the focus will be on three AI techniques, including ANNs, GP, and EPR. These three techniques are selected because they have been proved to be the most successful applied AI techniques in geotechnical engineering. Of these, ANN is by far the most commonly used one.

dc.publisherElsevier Science
dc.titleArtificial Intelligence in Geotechnical Engineering: Applications, Modeling Aspects, and Future Directions
dc.typeBook Chapter
dcterms.source.titleMetaheuristics in Water, Geotechnical and Transport Engineering
dcterms.source.placeLondon, UK
curtin.accessStatusFulltext not available

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