<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Under-reamed piles are extensively used to resist uplift forces and settlements. The objective of the present study is to develop various machine learning models (linear and non-linear) and determine the best model to estimate the ultimate uplift resistance of under-reamed piles embedded in cohesionless soil. The machine learning models were developed considering the following input parameters: the density index, dry density, base diameter, angle of an enlarged base with a vertical axis, shaft diameter, and embedment ratio. A linear equation is proposed to estimate the ultimate uplift resistance based on Multivariate Linear Regression analysis with a mean absolute error equaling 0.25kN and 0.50kN for loose and dense sands respectively. The Decision Tree Regression model provides an excellent degree of accuracy with a mean absolute error of 0.02kN and 0.02kN in cases of loose and dense sands respectively.</p>
</abstract>

Under-reamed piles are extensively used to resist uplift forces and settlements. The objective of the present study is to develop various machine learning models (linear and non-linear) and determine the best model to estimate the ultimate uplift resistance of under-reamed piles embedded in cohesionless soil. The machine learning models were developed considering the following input parameters: the density index, dry density, base diameter, angle of an enlarged base with a vertical axis, shaft diameter, and embedment ratio. A linear equation is proposed to estimate the ultimate uplift resistance based on Multivariate Linear Regression analysis with a mean absolute error equaling 0.25kN and 0.50kN for loose and dense sands respectively. The Decision Tree Regression model provides an excellent degree of accuracy with a mean absolute error of 0.02kN and 0.02kN in cases of loose and dense sands respectively.

Estimation of the Uplift Resistance for an Under-Reamed Pile in Dry Sand Using Machine Learning

MTech student, Department of Civil Engineering

Professor, Department of Civil Engineering

AICTE-INAE Distinguished Visiting Professor, Professor Emeritus, JNT University

Slovak Journal of Civil Engineering

This work is licensed under the Creative Commons Attribution 4.0 International License.

Under-reamed piles are extensively used to resist uplift forces and settlements. The objective of the present study is to develop various machine learning...

Estimation of the Uplift Resistance for an Under-Reamed Pile in Dry Sand Using Machine Learning

Data publikacji: 13 paź 2022

Zakres stron: 17 - 26

DOI: https://doi.org/10.2478/sjce-2022-0017

Słowa kluczowe
Under-reamedpile, Uplift resistance, Machine learning, Multivariate linear regression and decision tree regression

© 2022 Sharad Dadhich et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Estimation of the Uplift Resistance for an Under-Reamed Pile in Dry Sand Using Machine Learning

Data publikacji: 13 paź 2022

Zakres stron: 17 - 26

DOI: https://doi.org/10.2478/sjce-2022-0017

Słowa kluczoweUnder-reamedpile, Uplift resistance, Machine learning, Multivariate linear regression and decision tree regression

© 2022 Sharad Dadhich et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Under-reamedpile, Uplift resistance, Machine learning, Multivariate linear regression and decision tree regression