How does machine learning algorithms compare to traditional methods in predicting rut depth in asphalt mixture?
Best insight from top research papers
Machine learning algorithms outperform traditional methods in predicting rut depth in asphalt mixtures. Studies show that machine learning models like Random Forest, Decision Tree, XGBoost, Support Vector Machine, and K-Nearest Neighbor provide superior predictions compared to regression models, with coefficients of determination (R2) exceeding 0.75. Artificial Neural Networks (ANN) have been successfully utilized to establish predictive models for rut depth based on factors like temperature, traffic volume, resilient modulus, and asphalt thickness. By incorporating explainable AI methods like SHapley Additive exPlanations (SHAP) analysis, machine learning models enhance trust in predictions and facilitate timely maintenance planning to extend pavement life and reduce reconstruction costs. Overall, machine learning algorithms offer more accurate and reliable rut depth predictions for effective pavement management and maintenance decisions.
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| Papers (5) | Insight |
|---|---|
| Machine learning algorithms, particularly the neural network model, outperform traditional methods like multiple linear regression in predicting rut depth in asphalt mixtures, as shown in the research. | |
| Machine learning algorithms, specifically Gaussian radial basis neural networks, outperform traditional methods in predicting rut depth in asphalt mixtures, enhancing accuracy by 50% with an average RMSE of 3.95. | |
| Machine learning algorithms, specifically Artificial Neural Networks, outperform traditional methods in predicting rut depth in asphalt mixtures by capturing complex relationships between factors like temperature, load, and asphalt properties. | |
| Machine learning algorithms outperform traditional methods in predicting rut depth in asphalt mixture, with Random Forest showing the best performance due to its extrapolation and optimization capabilities. | |
| Machine learning algorithms, like artificial neural networks, offer improved rut depth prediction in asphalt mixtures compared to traditional empirical methods due to enhanced accuracy and predictive capabilities. |
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