Intelligent Computing Based Formulas to Predict the Settlement of Shallow Foundations on Cohesionless Soils
Bashar Tarawneh1, *, Wassel AL Bodour1, Khaled Al Ajmi2
Identifiers and Pagination:Year: 2019
First Page: 1
Last Page: 9
Publisher ID: TOCIEJ-13-1
Article History:Received Date: 05/10/2018
Revision Received Date: 18/12/2018
Acceptance Date: 16/01/2019
Electronic publication date: 20/02/2019
Collection year: 2019
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Although it is a regular duty of geotechnical engineers to evaluate how much shallow foundation settles in the granular soil, there is no well-approved formula for this task. The intent of this research is to develop a formula that is adequately simple to be used in routine geotechnical engineering work but complete enough to address the behavior of granular soil associated with the settlement issue.
Cone penetration test and foundation load test data were used to generate a formula that can predict the settlement. Genetic Programming (GP) based Symbolic Regression (GP-SR) and artificial neural networks were used to develop an optimized formula. Settlements were also calculated using the finite method and compared to the results of the developed formula.
Results and Conclusion:
Two formulas were developed using SR, and several models were developed using ANN. ANN model 1 has the highest R2 value (0.93) and the lowest MSE (0.16) among all developed ANN and GP-SR models. FEM settlements were almost double the measured ones in some instances.