Effective stress parameter in unsaturated soils : an evolutionary-based prediction model

Ahangar Asr, A ORCID: https://orcid.org/0000-0002-8210-7519 and Javadi, AA 2021, 'Effective stress parameter in unsaturated soils : an evolutionary-based prediction model' , Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction, 173 (4) , pp. 96-105.

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Deformations and failures in unsaturated soils are influenced directly by the effective stress calculated using the stress equation affected by the effective stress parameter. A data mining-based approach, the Evolutionary Polynomial Regression (EPR), is implemented in this research to develop a prediction model for the effective stress parameter in unsaturated soils. The proposed modelling approach takes an evolutionary computing technique to for finding polynomial models that are structured and explicit. A combination of the well-established genetic algorithm method and the least square approach are implemented to search for the most suitable polynomial structures and their corresponding parameters for all terms in the developed polynomial structure. A set of unsaturated soil experimental results (triaxial tests) from literature were used in this study to develop the prediction model. Once the model completed it was evaluated based on its performance for making predictions using input parameters that were previously kept unseen to validate generalization capabilities (making predictions of the output for new input data). The predictions made by the model, were compared to actual measured data from the lab tests as well as an Artificial Neural Network based model. A sensitivity analysis was also done to assess the level and form of contributions that input parameters had to the developed model. The results showed that the developed model could successfully and to a high level of accuracy capture and redevelop the intrinsic connections between the input parameters involved in the model to help produce accurate the effective stress parameter predictions that can not only compete with the artificial neural network model in terms of accuracy of the model predictions and generalisation capabilities; but also outperform the artificial neural network model with regards to the structure, simplicity and transparency.

Item Type: Article
Additional Information: ** From Crossref journal articles via Jisc Publications Router **Journal IDs: eissn 2397-8759 **History: issued 30-07-2021; published_online 30-07-2021
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction
Publisher: Thomas Telford Ltd.
ISSN: 2397-8759
Related URLs:
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 12 Aug 2021 08:23
Last Modified: 30 Jul 2022 02:30
URI: https://usir.salford.ac.uk/id/eprint/61464

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