Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Determination of Friction Capacity of Driven Pile in Clay Using Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR)
Abstract Friction capacity ($ f_{s} $) of driven pile in clay is key parameter for designing pile foundation. This study employs Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR) for determination of $ f_{s} $ of driven piles in clay. GPR is a Bayesian nonparametric regression model. MPMR is a probabilistic model. Pile length (L), pile diameter (D), effective vertical stress (σ’v), undrained shear strength ($ S_{u} $) have been used as input variables of GPR and MPMR. The output of the models is $ f_{s} $. The developed GPR, MPMR models have been compared with the Artificial Neural Network (ANN). GPR also gives the variance of predicted $ f_{s} $. The results prove that the developed GPR and MPMR are efficient models for prediction of $ f_{s} $ of driven piles in clay.
Determination of Friction Capacity of Driven Pile in Clay Using Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR)
Abstract Friction capacity ($ f_{s} $) of driven pile in clay is key parameter for designing pile foundation. This study employs Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR) for determination of $ f_{s} $ of driven piles in clay. GPR is a Bayesian nonparametric regression model. MPMR is a probabilistic model. Pile length (L), pile diameter (D), effective vertical stress (σ’v), undrained shear strength ($ S_{u} $) have been used as input variables of GPR and MPMR. The output of the models is $ f_{s} $. The developed GPR, MPMR models have been compared with the Artificial Neural Network (ANN). GPR also gives the variance of predicted $ f_{s} $. The results prove that the developed GPR and MPMR are efficient models for prediction of $ f_{s} $ of driven piles in clay.
Determination of Friction Capacity of Driven Pile in Clay Using Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR)
Samui, Pijush (Autor:in)
2019
Aufsatz (Zeitschrift)
Englisch
Modelling pile capacity using Gaussian process regression
| Elsevier | 2010
Modelling pile capacity using Gaussian process regression
| Online Contents | 2010