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Developing Prediction Equations for Soil Resilient Modulus Using Evolutionary Machine Learning
https://orcid.org/http://orcid.org/0000-0001-5958-4790
The soil resilient modulus (MR) is essential to pavement design. This parameter is determined through a costly and time-consuming repeated load triaxial test. Accordingly, prior research focused on implementing complex and interpretable machine learning (ML) models to predict MR directly from soil parameters. However, existing models rely on either black-box machine learning, sacrificing interpretability, or traditional genetic programming (GP) approaches with underfitting issues. This study introduces an innovative approach using the Adaptive Layered Population Structure Genetic Algorithm (ALPS-GA) to develop accurate and fully interpretable MR prediction models for cohesive soils. For this purpose, a soil dataset was adopted from the literature with 891 data points for the A-4, A-6, and A-7-6 soil classes. Three MR prediction equations were developed for each soil class, and the performance of each equation was evaluated using the coefficient of determination (R2), the root mean squared error (RMSE), and the mean absolute error (MAE). The R2 for the developed models ranged from 0.91 to 0.93 for the testing set; the RMSE ranged from 7.10 to 8.63 MPa, and the MAE ranged from 5.10 to 7.2 MPa, reflecting high-accuracy models. A comparative bias-variance analysis was done for the proposed models, and it was concluded that they do not tend to overfit or underfit the data, unlike previous models. Finally, a sensitivity analysis was implemented to investigate the impact of each soil parameter on MR for each soil type.
Developing Prediction Equations for Soil Resilient Modulus Using Evolutionary Machine Learning
https://orcid.org/http://orcid.org/0000-0001-5958-4790
The soil resilient modulus (MR) is essential to pavement design. This parameter is determined through a costly and time-consuming repeated load triaxial test. Accordingly, prior research focused on implementing complex and interpretable machine learning (ML) models to predict MR directly from soil parameters. However, existing models rely on either black-box machine learning, sacrificing interpretability, or traditional genetic programming (GP) approaches with underfitting issues. This study introduces an innovative approach using the Adaptive Layered Population Structure Genetic Algorithm (ALPS-GA) to develop accurate and fully interpretable MR prediction models for cohesive soils. For this purpose, a soil dataset was adopted from the literature with 891 data points for the A-4, A-6, and A-7-6 soil classes. Three MR prediction equations were developed for each soil class, and the performance of each equation was evaluated using the coefficient of determination (R2), the root mean squared error (RMSE), and the mean absolute error (MAE). The R2 for the developed models ranged from 0.91 to 0.93 for the testing set; the RMSE ranged from 7.10 to 8.63 MPa, and the MAE ranged from 5.10 to 7.2 MPa, reflecting high-accuracy models. A comparative bias-variance analysis was done for the proposed models, and it was concluded that they do not tend to overfit or underfit the data, unlike previous models. Finally, a sensitivity analysis was implemented to investigate the impact of each soil parameter on MR for each soil type.
Developing Prediction Equations for Soil Resilient Modulus Using Evolutionary Machine Learning
https://orcid.org/http://orcid.org/0000-0001-5958-4790
The soil resilient modulus (MR) is essential to pavement design. This parameter is determined through a costly and time-consuming repeated load triaxial test. Accordingly, prior research focused on implementing complex and interpretable machine learning (ML) models to predict MR directly from soil parameters. However, existing models rely on either black-box machine learning, sacrificing interpretability, or traditional genetic programming (GP) approaches with underfitting issues. This study introduces an innovative approach using the Adaptive Layered Population Structure Genetic Algorithm (ALPS-GA) to develop accurate and fully interpretable MR prediction models for cohesive soils. For this purpose, a soil dataset was adopted from the literature with 891 data points for the A-4, A-6, and A-7-6 soil classes. Three MR prediction equations were developed for each soil class, and the performance of each equation was evaluated using the coefficient of determination (R2), the root mean squared error (RMSE), and the mean absolute error (MAE). The R2 for the developed models ranged from 0.91 to 0.93 for the testing set; the RMSE ranged from 7.10 to 8.63 MPa, and the MAE ranged from 5.10 to 7.2 MPa, reflecting high-accuracy models. A comparative bias-variance analysis was done for the proposed models, and it was concluded that they do not tend to overfit or underfit the data, unlike previous models. Finally, a sensitivity analysis was implemented to investigate the impact of each soil parameter on MR for each soil type.
Developing Prediction Equations for Soil Resilient Modulus Using Evolutionary Machine Learning
Transp. Infrastruct. Geotech.
Sadik, Laith (author)
Transportation Infrastructure Geotechnology ; 11 ; 1598-1620
2024-08-01
23 pages
Article (Journal)
Electronic Resource
English
Developing Prediction Equations for Soil Resilient Modulus Using Evolutionary Machine Learning
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