Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of International Roughness Index Using CatBooster and Shap Values
https://orcid.org/http://orcid.org/0000-0002-3189-3433
International Roughness Index (IRI) is the performance index of pavements that exhibits the efficiency of pavement smoothness. Road roughness is a fundamental element used for determining the performance of pavements and the ride quality of road users, therefore, this research aims to develop the precise IRI prediction model for flexible pavements using advanced machine learning algorithms including supervised methods. This research is directed toward accessing the functional performance of the pavements through long-term pavement performance (LTPP) databases. For developing the model, the incorporated dataset includes a set of functional attributes from general pavement studies (GPS-1, GPS-2 and GPS-6) and specific pavement studies (SPS-1, SPS-3 and SPS-5). The developed algorithms showed that the machine learning algorithms are more precise and accurate in predicting the IRI than the traditional regression approaches. The machine learning algorithms use the shapely additive explanation (SHAP) values to access the feature significance for each independent element on the predictive performance. The analysis showed that CatBooster Regression outperformed the random forest regression, artificial neural network (ANN), and the simple regression models in terms of mean square error and prediction quality with a coefficient of determination up to 0.99. The study depicted that it is possible to correlate the roughness index with pavement and structural, climatic and distress parameters that can be utilized for pavement maintenance.
Prediction of International Roughness Index Using CatBooster and Shap Values
https://orcid.org/http://orcid.org/0000-0002-3189-3433
International Roughness Index (IRI) is the performance index of pavements that exhibits the efficiency of pavement smoothness. Road roughness is a fundamental element used for determining the performance of pavements and the ride quality of road users, therefore, this research aims to develop the precise IRI prediction model for flexible pavements using advanced machine learning algorithms including supervised methods. This research is directed toward accessing the functional performance of the pavements through long-term pavement performance (LTPP) databases. For developing the model, the incorporated dataset includes a set of functional attributes from general pavement studies (GPS-1, GPS-2 and GPS-6) and specific pavement studies (SPS-1, SPS-3 and SPS-5). The developed algorithms showed that the machine learning algorithms are more precise and accurate in predicting the IRI than the traditional regression approaches. The machine learning algorithms use the shapely additive explanation (SHAP) values to access the feature significance for each independent element on the predictive performance. The analysis showed that CatBooster Regression outperformed the random forest regression, artificial neural network (ANN), and the simple regression models in terms of mean square error and prediction quality with a coefficient of determination up to 0.99. The study depicted that it is possible to correlate the roughness index with pavement and structural, climatic and distress parameters that can be utilized for pavement maintenance.
Prediction of International Roughness Index Using CatBooster and Shap Values
https://orcid.org/http://orcid.org/0000-0002-3189-3433
International Roughness Index (IRI) is the performance index of pavements that exhibits the efficiency of pavement smoothness. Road roughness is a fundamental element used for determining the performance of pavements and the ride quality of road users, therefore, this research aims to develop the precise IRI prediction model for flexible pavements using advanced machine learning algorithms including supervised methods. This research is directed toward accessing the functional performance of the pavements through long-term pavement performance (LTPP) databases. For developing the model, the incorporated dataset includes a set of functional attributes from general pavement studies (GPS-1, GPS-2 and GPS-6) and specific pavement studies (SPS-1, SPS-3 and SPS-5). The developed algorithms showed that the machine learning algorithms are more precise and accurate in predicting the IRI than the traditional regression approaches. The machine learning algorithms use the shapely additive explanation (SHAP) values to access the feature significance for each independent element on the predictive performance. The analysis showed that CatBooster Regression outperformed the random forest regression, artificial neural network (ANN), and the simple regression models in terms of mean square error and prediction quality with a coefficient of determination up to 0.99. The study depicted that it is possible to correlate the roughness index with pavement and structural, climatic and distress parameters that can be utilized for pavement maintenance.
Prediction of International Roughness Index Using CatBooster and Shap Values
Int. J. Pavement Res. Technol.
Bral, Saket (Autor:in) / Kumar, Patnala Phani (Autor:in) / Chopra, Tanuj (Autor:in)
International Journal of Pavement Research and Technology ; 17 ; 518-533
01.03.2024
16 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Long term pavement performance (LTTP) , Multiple linear regression (MLR) , Random forest (RF) , Artificial neural network (ANN) , Genetic programming (GP) , Shapely additive explanations (SHAP) , Group method of data handling (GMDH) , Gradient boosting decision tree (GBDT) Engineering , Civil Engineering , Structural Materials , Building Construction and Design
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