Flexible Pavements Performance Prediction Using Machine Learning: Supervised Learning with Tree-Based Algorithms

This paper presents the application of supervised machine learning tree-based algorithms to predict the performance of flexible pavements, utilizing data from 55 experimental sections from the Long-Term Pavement Performance (LTPP) program. The International Roughness Index (IRI) was adopted as a pavement performance indicator. Decision Tree, Random Forest, and XGBoost algorithms were employed in this study. Ultimately, the best-trained model was XGBoost, achieving an R-squared of 0.98 and an RMSE of 0.08 for the test sample.

Paper presented at the World Conference on Transport Research - WCTR 2023 Montreal 17-21 July 2023.

Citation:

Tiago Tamagusko and Adelino Ferreira (2023). Pavement Performance Prediction using Machine Learning: Supervised Learning with Tree-Based Algorithms. World Conference on Transport Research - WCTR 2023.

@article{Tamagusko-Ferreira2023-predict-iri-tree,
  author = Tiago Tamagusko, Adelino Ferreira,
  title = "Pavement Performance Prediction using Machine Learning: Supervised Learning with Tree-Based Algorithms",
  journal = {World Conference on Transport Research - WCTR 2023},
  year = 2023,
  address   = "Canada, Montreal"
}

How to use

Create a virtual environment: python -m venv venv
Activate the virtual environment:
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- Linux/Mac: source venv/bin/activate
Install the required packages: pip install -r requirements.txt
Run the Jupyter Notebook: jupyter notebook main.ipynb

Please direct issues, bug reports and pull requests to this GitHub page. To contact me directly, send email to tamagusko@gmail.com.

-- Tiago

tamagusko/predict-iri-trees

Flexible Pavements Performance Prediction Using Machine Learning: Supervised Learning with Tree-Based Algorithms

Citation:

How to use

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