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Prediction of the Bearing Capacity of Composite Grounds Made of Geogrid-Reinforced Sand over Encased Stone Columns Floating in Soft Soil Using a White-Box Machine Learning Model

Authors :
Husein Ali Zeini
Nabeel Katfan Lwti
Hamza Imran
Sadiq N. Henedy
Luís Filipe Almeida Bernardo
Zainab Al-Khafaji
Source :
Applied Sciences, Vol 13, Iss 8, p 5131 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Stone columns have been extensively advocated as a traditional approach to increase the undrained bearing capacity and reduce the settlement of footings sitting on cohesive ground. However, due to the complex interaction between the soil and the stone columns, there currently needs to be a commonly acknowledged approach that can be used to precisely predict the undrained bearing capacity of the system. For this reason, the bearing capacity of a sandy bed reinforced with geogrid and sitting above a collection of geogrid-encased stone columns floating in soft clay was studied in this research. Using a white-box machine learning (ML) technique called Multivariate Polynomial Regression (MPR), this work aims to develop a model for predicting the bearing capacity of the referred foundation system. For this purpose, two hundred and forty-five experimental results were collected from the literature. In addition, the model was compared to two other ML models, namely, a black-box model known as Random Forest (RF) and a white-box ML model called Linear Regression (LR). In terms of R2 (coefficient of determination) and RMSE (Root Mean Absolute Error) values, the newly proposed model outperforms the two other referred models and demonstrates robust estimation capabilities. In addition, a parametric analysis was carried out to determine the contribution of each input variable and its relative significance on the output.

Details

Language :
English
ISSN :
13085131 and 20763417
Volume :
13
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.0dd661f871c4409fbe6bf35474648345
Document Type :
article
Full Text :
https://doi.org/10.3390/app13085131