Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation
This study examines the capability of the Relevance Vector Machine (RVM) and Multivariate Adaptive Regression Spline (MARS) for prediction of ultimate capacity of driven piles and drilled shafts. RVM is a sparse method for training generalized linear models, while MARS technique is basically an adap...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
Khaje Nasirodin Toosi University of Technology
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/42029 |
| _version_ | 1848756306168512512 |
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| author | Samui, P. Shahin, Mohamed |
| author_facet | Samui, P. Shahin, Mohamed |
| author_sort | Samui, P. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This study examines the capability of the Relevance Vector Machine (RVM) and Multivariate Adaptive Regression Spline (MARS) for prediction of ultimate capacity of driven piles and drilled shafts. RVM is a sparse method for training generalized linear models, while MARS technique is basically an adaptive piece-wise regression approach. In this paper, pile capacity prediction models are developed based on data obtained from the literature and comprise in-situ pile loading tests and Cone Penetration Test (CPT) results. Equations are derived from the developed RVM and MARS models, and the prediction results are compared with those obtained from available CPT-based methods. Sensitivity has been carried out to determine the effect of each input parameter. This study confirms that the developed RVM and MARS models predict ultimate capacity of driven piles and drilled shafts reasonably well, and outperform the available methods. |
| first_indexed | 2025-11-14T09:10:06Z |
| format | Journal Article |
| id | curtin-20.500.11937-42029 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:10:06Z |
| publishDate | 2014 |
| publisher | Khaje Nasirodin Toosi University of Technology |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-420292017-01-30T14:56:55Z Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation Samui, P. Shahin, Mohamed Prediction multivariate adaptive regression spline axial capacity pile foundations relevance vector machine This study examines the capability of the Relevance Vector Machine (RVM) and Multivariate Adaptive Regression Spline (MARS) for prediction of ultimate capacity of driven piles and drilled shafts. RVM is a sparse method for training generalized linear models, while MARS technique is basically an adaptive piece-wise regression approach. In this paper, pile capacity prediction models are developed based on data obtained from the literature and comprise in-situ pile loading tests and Cone Penetration Test (CPT) results. Equations are derived from the developed RVM and MARS models, and the prediction results are compared with those obtained from available CPT-based methods. Sensitivity has been carried out to determine the effect of each input parameter. This study confirms that the developed RVM and MARS models predict ultimate capacity of driven piles and drilled shafts reasonably well, and outperform the available methods. 2014 Journal Article http://hdl.handle.net/20.500.11937/42029 Khaje Nasirodin Toosi University of Technology restricted |
| spellingShingle | Prediction multivariate adaptive regression spline axial capacity pile foundations relevance vector machine Samui, P. Shahin, Mohamed Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation |
| title | Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation |
| title_full | Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation |
| title_fullStr | Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation |
| title_full_unstemmed | Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation |
| title_short | Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation |
| title_sort | relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation |
| topic | Prediction multivariate adaptive regression spline axial capacity pile foundations relevance vector machine |
| url | http://hdl.handle.net/20.500.11937/42029 |