Artificial Intelligence in Geotechnical Engineering: Applications, Modeling Aspects, and Future Directions
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 ab...
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| Format: | Book Chapter |
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Elsevier Science
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/33251 |
| _version_ | 1848753893405622272 |
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| author | Shahin, Mohamed |
| author2 | Xin-She Yang |
| author_facet | Xin-She Yang Shahin, Mohamed |
| author_sort | Shahin, Mohamed |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T08:31:45Z |
| format | Book Chapter |
| id | curtin-20.500.11937-33251 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:31:45Z |
| publishDate | 2012 |
| publisher | Elsevier Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-332512023-02-07T08:01:19Z Artificial Intelligence in Geotechnical Engineering: Applications, Modeling Aspects, and Future Directions Shahin, Mohamed Xin-She Yang Amir Hossein Gandomi Siamak Talatahari Amir Hossein Alavi 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. 2012 Book Chapter http://hdl.handle.net/20.500.11937/33251 Elsevier Science restricted |
| spellingShingle | Shahin, Mohamed Artificial Intelligence in Geotechnical Engineering: Applications, Modeling Aspects, and Future Directions |
| title | Artificial Intelligence in Geotechnical Engineering: Applications, Modeling Aspects, and Future Directions |
| title_full | Artificial Intelligence in Geotechnical Engineering: Applications, Modeling Aspects, and Future Directions |
| title_fullStr | Artificial Intelligence in Geotechnical Engineering: Applications, Modeling Aspects, and Future Directions |
| title_full_unstemmed | Artificial Intelligence in Geotechnical Engineering: Applications, Modeling Aspects, and Future Directions |
| title_short | Artificial Intelligence in Geotechnical Engineering: Applications, Modeling Aspects, and Future Directions |
| title_sort | artificial intelligence in geotechnical engineering: applications, modeling aspects, and future directions |
| url | http://hdl.handle.net/20.500.11937/33251 |