Issue |
BIO Web Conf.
Volume 151, 2025
International Conference “Mountains: Biodiversity, Landscapes and Cultures” (MBLC-2024)
|
|
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Article Number | 04005 | |
Number of page(s) | 8 | |
Section | Health and Biochemistry | |
DOI | https://doi.org/10.1051/bioconf/202515104005 | |
Published online | 21 January 2025 |
Machine Learning-Driven Resilient Modulus Prediction for Flexible Pavements Across Mountainous and Other Regions
1 Department of Civil Engineering, Nazarbayev University, Kazakhstan
2 Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, KP Pakistan
3 Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan
* Corresponding author: usama.asif@alumni.nu.edu.kz
Accurate estimation of the elastic modulus (Mr) in the com- pacted subgrade soil is essential for the design of flexible pavement systems that are both reliable and environmentally friendly. Mr significantly affects the structural integrity of the pavement, especially in hilly areas with varying loads and climatic conditions. This study collects 2813 data points from pre- vious research results to create an accurate prediction model. The gradient boosted (GB) machine learning (ML) approach is selected to predict the Mr of the compacted subgrade soil. The accuracy and predictive performance of the GB model were evaluated using statistical analysis that includes fun- damental metrics such as root mean square error, mean absolute error, and relative squared error. The model obtained R² values of 0.96 and 0.94 for the training and testing datasets. The RMSE was 5 MPa for training and 7.48 MPa for testing, while the MAE was 3.18 MPa and 5.55 MPa. These results highlight the potential of GB in predicting soil Mr, thereby supporting the development of more accurate and efficient Mr prediction, ultimately reduc- ing time and cost.
Key words: Resilient modulus / Gradient boosting / Machine learning
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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