A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
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 (author)
2019
Article (Journal)
English
Modelling pile capacity using Gaussian process regression
| Online Contents | 2010
Modelling pile capacity using Gaussian process regression
| Elsevier | 2010
Modelling pile capacity using Gaussian process regression
| Elsevier | 2010