Intelligent computing for modeling axial capacity of pile foundations
In the last few decades, numerous methods have been developed for predicting the axial capacity of pile foundations. Among the available methods, the cone penetration test (CPT)-based models have been shown to give better predictions in many situations. This can be attributed to the fact that CPT-ba...
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| Format: | Journal Article |
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NRC Canada
2010
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| Online Access: | http://hdl.handle.net/20.500.11937/20665 |
| _version_ | 1848750371418144768 |
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| author | Shahin, Mohamed |
| author_facet | Shahin, Mohamed |
| author_sort | Shahin, Mohamed |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In the last few decades, numerous methods have been developed for predicting the axial capacity of pile foundations. Among the available methods, the cone penetration test (CPT)-based models have been shown to give better predictions in many situations. This can be attributed to the fact that CPT-based methods have been developed in accordance with the CPT results, which have been found to yield more reliable soil properties; hence, more accurate axial pile capacity predictions. In this paper, one of the most commonly used artificial intelligence techniques, i.e., artificial neural networks (ANNs), is utilized in an attempt to develop artificial neural network (ANN) models that provide more accurate axial capacity predictions for driven piles and drilled shafts. The ANN models are developed using data collected from the literature and comprise 80 driven pile and 94 drilled-shaft load tests, as well as CPT results. The predictions from the ANN models are compared with those obtained from the most commonly used available CPT-based methods, and statistical analyses are carried out to rank and evaluate the performance of the ANN models and CPT methods. To facilitate the use of the developed ANN models, they are translated into simple design equations suitable for hand calculations. |
| first_indexed | 2025-11-14T07:35:46Z |
| format | Journal Article |
| id | curtin-20.500.11937-20665 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:35:46Z |
| publishDate | 2010 |
| publisher | NRC Canada |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-206652017-09-13T15:57:56Z Intelligent computing for modeling axial capacity of pile foundations Shahin, Mohamed driven piles drilled shafts artificial neural networks axial capacity modeling In the last few decades, numerous methods have been developed for predicting the axial capacity of pile foundations. Among the available methods, the cone penetration test (CPT)-based models have been shown to give better predictions in many situations. This can be attributed to the fact that CPT-based methods have been developed in accordance with the CPT results, which have been found to yield more reliable soil properties; hence, more accurate axial pile capacity predictions. In this paper, one of the most commonly used artificial intelligence techniques, i.e., artificial neural networks (ANNs), is utilized in an attempt to develop artificial neural network (ANN) models that provide more accurate axial capacity predictions for driven piles and drilled shafts. The ANN models are developed using data collected from the literature and comprise 80 driven pile and 94 drilled-shaft load tests, as well as CPT results. The predictions from the ANN models are compared with those obtained from the most commonly used available CPT-based methods, and statistical analyses are carried out to rank and evaluate the performance of the ANN models and CPT methods. To facilitate the use of the developed ANN models, they are translated into simple design equations suitable for hand calculations. 2010 Journal Article http://hdl.handle.net/20.500.11937/20665 10.1139/T09-094 NRC Canada fulltext |
| spellingShingle | driven piles drilled shafts artificial neural networks axial capacity modeling Shahin, Mohamed Intelligent computing for modeling axial capacity of pile foundations |
| title | Intelligent computing for modeling axial capacity of pile foundations |
| title_full | Intelligent computing for modeling axial capacity of pile foundations |
| title_fullStr | Intelligent computing for modeling axial capacity of pile foundations |
| title_full_unstemmed | Intelligent computing for modeling axial capacity of pile foundations |
| title_short | Intelligent computing for modeling axial capacity of pile foundations |
| title_sort | intelligent computing for modeling axial capacity of pile foundations |
| topic | driven piles drilled shafts artificial neural networks axial capacity modeling |
| url | http://hdl.handle.net/20.500.11937/20665 |