Genetic programming for predicting axial capacity of driven piles
The behavior of pile foundations under axial loading is complex and not yet entirely understood. Most available methods for predicting axial capacity of driven piles have failed to achieve consistent success in relation to accurate pile capacity prediction. However, among available methods, the cone...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
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IC2E International Centre for Computational Engineering
2009
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| Online Access: | http://hdl.handle.net/20.500.11937/26448 |
| _version_ | 1848751989417050112 |
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| author | Alkroosh, Iyad Shahin, Mohamed Nikraz, Hamid |
| author2 | S Pietruszczak |
| author_facet | S Pietruszczak Alkroosh, Iyad Shahin, Mohamed Nikraz, Hamid |
| author_sort | Alkroosh, Iyad |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The behavior of pile foundations under axial loading is complex and not yet entirely understood. Most available methods for predicting axial capacity of driven piles have failed to achieve consistent success in relation to accurate pile capacity prediction. However, among available methods, the cone penetration test (CPT) based models have shown to give better predictions in many situations. In an attempt to obtain more accurate axial pile capacity predictions from CPT test results, the genetic programming (GP) technique is used in this study. GP is a relatively new artificial intelligent computational technique that has been recently used with success in the field of geotechnical engineering. The data used for development of the GP model are collected from the literature and comprise a number of 78 pile load tests and CPT results. The model robustness is further investigated via a sensitivity analysis, and the performance of the GP model is compared with three of the most commonly used CPT-based traditional methods. The results indicate that the GP model provides more accurate axial capacity predictions of driven piles and outperforms the traditional methods. |
| first_indexed | 2025-11-14T08:01:29Z |
| format | Conference Paper |
| id | curtin-20.500.11937-26448 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:01:29Z |
| publishDate | 2009 |
| publisher | IC2E International Centre for Computational Engineering |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-264482022-12-09T06:09:41Z Genetic programming for predicting axial capacity of driven piles Alkroosh, Iyad Shahin, Mohamed Nikraz, Hamid S Pietruszczak G N Pande C Tamagnini R Wan The behavior of pile foundations under axial loading is complex and not yet entirely understood. Most available methods for predicting axial capacity of driven piles have failed to achieve consistent success in relation to accurate pile capacity prediction. However, among available methods, the cone penetration test (CPT) based models have shown to give better predictions in many situations. In an attempt to obtain more accurate axial pile capacity predictions from CPT test results, the genetic programming (GP) technique is used in this study. GP is a relatively new artificial intelligent computational technique that has been recently used with success in the field of geotechnical engineering. The data used for development of the GP model are collected from the literature and comprise a number of 78 pile load tests and CPT results. The model robustness is further investigated via a sensitivity analysis, and the performance of the GP model is compared with three of the most commonly used CPT-based traditional methods. The results indicate that the GP model provides more accurate axial capacity predictions of driven piles and outperforms the traditional methods. 2009 Conference Paper http://hdl.handle.net/20.500.11937/26448 IC2E International Centre for Computational Engineering restricted |
| spellingShingle | Alkroosh, Iyad Shahin, Mohamed Nikraz, Hamid Genetic programming for predicting axial capacity of driven piles |
| title | Genetic programming for predicting axial capacity of driven piles |
| title_full | Genetic programming for predicting axial capacity of driven piles |
| title_fullStr | Genetic programming for predicting axial capacity of driven piles |
| title_full_unstemmed | Genetic programming for predicting axial capacity of driven piles |
| title_short | Genetic programming for predicting axial capacity of driven piles |
| title_sort | genetic programming for predicting axial capacity of driven piles |
| url | http://hdl.handle.net/20.500.11937/26448 |