Load-settlement modelling of axially loaded drilled shafts using CPT-based recurrent neural networks
The design of pile foundations requires good estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement have been traditionally carried out separately. However, soil resistance and settlement are influenced by each other, and the design of pil...
| Main Author: | |
|---|---|
| Format: | Journal Article |
| Published: |
American Society of Civil Engineers
2014
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/17899 |
| _version_ | 1848749591190568960 |
|---|---|
| author | Shahin, Mohamed |
| author_facet | Shahin, Mohamed |
| author_sort | Shahin, Mohamed |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The design of pile foundations requires good estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement have been traditionally carried out separately. However, soil resistance and settlement are influenced by each other, and the design of pile foundations should thus consider the bearing capacity and settlement inseparably. This requires the full load–settlement response of piles to be well predicted. However, it is well known that the actual load–settlement response of pile foundations can be obtained only by load tests carried out in situ, which are expensive and time-consuming. In this paper, recurrent neural networks (RNNs) were used to develop a prediction model that can resemble the full load–settlement response of drilled shafts (bored piles) subjected to axial loading. The developed RNN model was calibrated and validated using several in situ full-scale pile load tests, as well as cone penetration test (CPT) data. The results indicate that the developed RNN model has the ability to reliably predict the load–settlement response of axially loaded drilled shafts and can thus be used by geotechnical engineers for routine design practice. |
| first_indexed | 2025-11-14T07:23:22Z |
| format | Journal Article |
| id | curtin-20.500.11937-17899 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:23:22Z |
| publishDate | 2014 |
| publisher | American Society of Civil Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-178992017-09-13T15:44:34Z Load-settlement modelling of axially loaded drilled shafts using CPT-based recurrent neural networks Shahin, Mohamed Geotechnical modeling Recurrent neural networks Load–settlement Bored piles Drilled shafts The design of pile foundations requires good estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement have been traditionally carried out separately. However, soil resistance and settlement are influenced by each other, and the design of pile foundations should thus consider the bearing capacity and settlement inseparably. This requires the full load–settlement response of piles to be well predicted. However, it is well known that the actual load–settlement response of pile foundations can be obtained only by load tests carried out in situ, which are expensive and time-consuming. In this paper, recurrent neural networks (RNNs) were used to develop a prediction model that can resemble the full load–settlement response of drilled shafts (bored piles) subjected to axial loading. The developed RNN model was calibrated and validated using several in situ full-scale pile load tests, as well as cone penetration test (CPT) data. The results indicate that the developed RNN model has the ability to reliably predict the load–settlement response of axially loaded drilled shafts and can thus be used by geotechnical engineers for routine design practice. 2014 Journal Article http://hdl.handle.net/20.500.11937/17899 10.1061/(ASCE)GM.1943-5622.0000370 American Society of Civil Engineers fulltext |
| spellingShingle | Geotechnical modeling Recurrent neural networks Load–settlement Bored piles Drilled shafts Shahin, Mohamed Load-settlement modelling of axially loaded drilled shafts using CPT-based recurrent neural networks |
| title | Load-settlement modelling of axially loaded drilled shafts using CPT-based recurrent neural networks |
| title_full | Load-settlement modelling of axially loaded drilled shafts using CPT-based recurrent neural networks |
| title_fullStr | Load-settlement modelling of axially loaded drilled shafts using CPT-based recurrent neural networks |
| title_full_unstemmed | Load-settlement modelling of axially loaded drilled shafts using CPT-based recurrent neural networks |
| title_short | Load-settlement modelling of axially loaded drilled shafts using CPT-based recurrent neural networks |
| title_sort | load-settlement modelling of axially loaded drilled shafts using cpt-based recurrent neural networks |
| topic | Geotechnical modeling Recurrent neural networks Load–settlement Bored piles Drilled shafts |
| url | http://hdl.handle.net/20.500.11937/17899 |