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A platform for research: civil engineering, architecture and urbanism
An ensemble learning model for asphalt pavement performance prediction based on gradient boosting decision tree
This paper proposes an ensemble learning model that deploys a Gradient Boosting Decision Tree (GBDT) to predict two relevant functional indices, the International roughness index (IRI) and the rut depth (RD), considering multiple influence factors. To train and validate the proposed models, more than 1600 different records were extracted from Long-Term Pavement Performance database. The most suitable hyper parameters for the GBDT model are determined through a grid search and 5-fold cross-validation. Then, a sensitivity analysis is performed to determine the final input variables among the initial considered factors. Further, the optimized models utilise SHAP (Shapley Additive explanation) to interpret the results and analyse the importance of influencing factors. Finally, a comparison experiment with reference artificial intelligence approaches demonstrates that, the GBDT model can outperform the artificial neural network (ANN) and the random forest regression (RFR) methods in terms of quality of prediction results, reaching a coefficient of determination (R2) equal to 0.9. The proposed model can provide more precise pavement performance values and may be useful for providing accurate reference for pavement maintenance and optimising the available budget for road administrations.
An ensemble learning model for asphalt pavement performance prediction based on gradient boosting decision tree
This paper proposes an ensemble learning model that deploys a Gradient Boosting Decision Tree (GBDT) to predict two relevant functional indices, the International roughness index (IRI) and the rut depth (RD), considering multiple influence factors. To train and validate the proposed models, more than 1600 different records were extracted from Long-Term Pavement Performance database. The most suitable hyper parameters for the GBDT model are determined through a grid search and 5-fold cross-validation. Then, a sensitivity analysis is performed to determine the final input variables among the initial considered factors. Further, the optimized models utilise SHAP (Shapley Additive explanation) to interpret the results and analyse the importance of influencing factors. Finally, a comparison experiment with reference artificial intelligence approaches demonstrates that, the GBDT model can outperform the artificial neural network (ANN) and the random forest regression (RFR) methods in terms of quality of prediction results, reaching a coefficient of determination (R2) equal to 0.9. The proposed model can provide more precise pavement performance values and may be useful for providing accurate reference for pavement maintenance and optimising the available budget for road administrations.
An ensemble learning model for asphalt pavement performance prediction based on gradient boosting decision tree
Guo, Runhua (author) / Fu, Donglei (author) / Sollazzo, Giuseppe (author)
International Journal of Pavement Engineering ; 23 ; 3633-3646
2022-08-24
14 pages
Article (Journal)
Electronic Resource
Unknown
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