A review of artificial intelligence applications in shallow foundations
Geotechnical engineering deals with materials (e.g. soil and rock) that, by their very nature, exhibit varied and uncertain behavior because of the imprecise physical processes associated with the formation of these materials. Modeling the behavior of such materials in geotechnical engineering appli...
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| Format: | Journal Article |
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J Ross Publishing Inc/Maney
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/5992 |
| _version_ | 1848744950504620032 |
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| author | Shahin, Mohamed |
| author_facet | 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 because of the imprecise physical processes associated with the formation of these materials. Modeling the behavior of such materials in geotechnical engineering applications is complex and sometimes 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 applications, including foundations, because it has demonstrated superior predictive ability compared to traditional methods. The main aim of this paper is to review the AI applications in shallow foundations and present the salient features associated with the AI modeling development. The paper also discusses the strengths and limitations of AI techniques compared to other modeling approaches. |
| first_indexed | 2025-11-14T06:09:36Z |
| format | Journal Article |
| id | curtin-20.500.11937-5992 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:09:36Z |
| publishDate | 2015 |
| publisher | J Ross Publishing Inc/Maney |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-59922017-09-13T14:42:52Z A review of artificial intelligence applications in shallow foundations Shahin, Mohamed Genetic programing Artificial intelligence Neural networks Shallow foundations Evolutionary polynomial regression Modeling Geotechnical engineering deals with materials (e.g. soil and rock) that, by their very nature, exhibit varied and uncertain behavior because of the imprecise physical processes associated with the formation of these materials. Modeling the behavior of such materials in geotechnical engineering applications is complex and sometimes 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 applications, including foundations, because it has demonstrated superior predictive ability compared to traditional methods. The main aim of this paper is to review the AI applications in shallow foundations and present the salient features associated with the AI modeling development. The paper also discusses the strengths and limitations of AI techniques compared to other modeling approaches. 2015 Journal Article http://hdl.handle.net/20.500.11937/5992 10.1179/1939787914Y.0000000058 J Ross Publishing Inc/Maney fulltext |
| spellingShingle | Genetic programing Artificial intelligence Neural networks Shallow foundations Evolutionary polynomial regression Modeling Shahin, Mohamed A review of artificial intelligence applications in shallow foundations |
| title | A review of artificial intelligence applications in shallow foundations |
| title_full | A review of artificial intelligence applications in shallow foundations |
| title_fullStr | A review of artificial intelligence applications in shallow foundations |
| title_full_unstemmed | A review of artificial intelligence applications in shallow foundations |
| title_short | A review of artificial intelligence applications in shallow foundations |
| title_sort | review of artificial intelligence applications in shallow foundations |
| topic | Genetic programing Artificial intelligence Neural networks Shallow foundations Evolutionary polynomial regression Modeling |
| url | http://hdl.handle.net/20.500.11937/5992 |