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Self-Learning Framework for Estimating Load Transfer Curves from Uninstrumented Pile Loading Tests
An attempt is made to estimate the load transfer curves from a database of uninstrumented pile-loading tests using a self-learning algorithm, an inverse analysis framework capable of handling ill-posed problems. The aim is to extract useful but masked information about load transfer encapsulated in the observed load-deformation response of axially loaded piles. From the results obtained, the estimated load transfer curves tend to produce better load deformation models compared with the predictions based on conventional t-z curves, thus indicating that the self-learning algorithm has succeeded in extracting the load transfer data indirectly from uninstrumented pile tests.
Self-Learning Framework for Estimating Load Transfer Curves from Uninstrumented Pile Loading Tests
An attempt is made to estimate the load transfer curves from a database of uninstrumented pile-loading tests using a self-learning algorithm, an inverse analysis framework capable of handling ill-posed problems. The aim is to extract useful but masked information about load transfer encapsulated in the observed load-deformation response of axially loaded piles. From the results obtained, the estimated load transfer curves tend to produce better load deformation models compared with the predictions based on conventional t-z curves, thus indicating that the self-learning algorithm has succeeded in extracting the load transfer data indirectly from uninstrumented pile tests.
Self-Learning Framework for Estimating Load Transfer Curves from Uninstrumented Pile Loading Tests
Ismail, Abdussamad (author)
Geo-Congress 2014 ; 2014 ; Atlanta, Georgia
Geo-Congress 2014 Technical Papers ; 1806-1815
2014-02-24
Conference paper
Electronic Resource
English
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