Intelligent computing for predicting axial capacity of drilled shafts
In the last few decades, numerous methods have been developed for predicting the axial capacity of drilled shafts. 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-base...
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| Other Authors: | |
| Format: | Conference Paper |
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American Society of Civil Engineers (ASCE)
2009
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| Online Access: | http://hdl.handle.net/20.500.11937/7090 |
| _version_ | 1848745265844977664 |
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| author | Shahin, Mohamed Jaksa, M. |
| author2 | Magued Iskander |
| author_facet | Magued Iskander Shahin, Mohamed Jaksa, M. |
| 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 drilled shafts. 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 results of the CPT tests, which have been found to yield more reliable soil properties, hence, more accurate axial capacity predictions of drilled shafts. In this paper, one of the most commonly used artificial intelligence techniques, i.e. artificial neural networks (ANNs), was utilized in an attempt to obtain more accurate axial capacity predictions for drilled shafts. The ANN model was developed using data collected from the literature that comprise CPT results and drilled shaft load tests of 94 case records. The predictions from the ANN model were compared with those obtained from three commonly used available CPT-based methods. The results indicate that the ANN-based model provides more accurate axial capacity predictions of drilled shafts and outperforms the available conventional methods. |
| first_indexed | 2025-11-14T06:14:37Z |
| format | Conference Paper |
| id | curtin-20.500.11937-7090 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:14:37Z |
| publishDate | 2009 |
| publisher | American Society of Civil Engineers (ASCE) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-70902022-12-09T05:23:40Z Intelligent computing for predicting axial capacity of drilled shafts Shahin, Mohamed Jaksa, M. Magued Iskander Debra F Laefer Mohamad H Hussein In the last few decades, numerous methods have been developed for predicting the axial capacity of drilled shafts. 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 results of the CPT tests, which have been found to yield more reliable soil properties, hence, more accurate axial capacity predictions of drilled shafts. In this paper, one of the most commonly used artificial intelligence techniques, i.e. artificial neural networks (ANNs), was utilized in an attempt to obtain more accurate axial capacity predictions for drilled shafts. The ANN model was developed using data collected from the literature that comprise CPT results and drilled shaft load tests of 94 case records. The predictions from the ANN model were compared with those obtained from three commonly used available CPT-based methods. The results indicate that the ANN-based model provides more accurate axial capacity predictions of drilled shafts and outperforms the available conventional methods. 2009 Conference Paper http://hdl.handle.net/20.500.11937/7090 10.1061/41022(336)4 American Society of Civil Engineers (ASCE) fulltext |
| spellingShingle | Shahin, Mohamed Jaksa, M. Intelligent computing for predicting axial capacity of drilled shafts |
| title | Intelligent computing for predicting axial capacity of drilled shafts |
| title_full | Intelligent computing for predicting axial capacity of drilled shafts |
| title_fullStr | Intelligent computing for predicting axial capacity of drilled shafts |
| title_full_unstemmed | Intelligent computing for predicting axial capacity of drilled shafts |
| title_short | Intelligent computing for predicting axial capacity of drilled shafts |
| title_sort | intelligent computing for predicting axial capacity of drilled shafts |
| url | http://hdl.handle.net/20.500.11937/7090 |