Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of Rutting Depth Using Random Forest Methods in Full-Depth Reclamation Asphalt Pavements
https://orcid.org/http://orcid.org/0000-0003-0078-1647
https://orcid.org/http://orcid.org/0000-0002-4334-4474
Traditional pavement design relies on computational deterioration models that were trained using relatively small datasets and solid mechanic theory. This paper analyzes full-depth reclamation (FDR), a pavement recycling rehabilitation alternative that is not modeled effectively in current design models. A random forest algorithm is calibrated based on empirical data collected from 10 FDR sites in Colorado. The resulting model yields significant improvements compared to the mechanistic-empirical model currently used in pavement design, with a reduction in RMSE of 86.7%. The study found that precipitation and freezing index have the greatest impact on rutting depth at the end of the pavement design life, with changes in traffic having a significantly lower impact on rutting. This study provides a framework for utilizing large amounts of condition, climatic, and traffic data to model pavement deterioration.
Prediction of Rutting Depth Using Random Forest Methods in Full-Depth Reclamation Asphalt Pavements
https://orcid.org/http://orcid.org/0000-0003-0078-1647
https://orcid.org/http://orcid.org/0000-0002-4334-4474
Traditional pavement design relies on computational deterioration models that were trained using relatively small datasets and solid mechanic theory. This paper analyzes full-depth reclamation (FDR), a pavement recycling rehabilitation alternative that is not modeled effectively in current design models. A random forest algorithm is calibrated based on empirical data collected from 10 FDR sites in Colorado. The resulting model yields significant improvements compared to the mechanistic-empirical model currently used in pavement design, with a reduction in RMSE of 86.7%. The study found that precipitation and freezing index have the greatest impact on rutting depth at the end of the pavement design life, with changes in traffic having a significantly lower impact on rutting. This study provides a framework for utilizing large amounts of condition, climatic, and traffic data to model pavement deterioration.
Prediction of Rutting Depth Using Random Forest Methods in Full-Depth Reclamation Asphalt Pavements
https://orcid.org/http://orcid.org/0000-0003-0078-1647
https://orcid.org/http://orcid.org/0000-0002-4334-4474
Traditional pavement design relies on computational deterioration models that were trained using relatively small datasets and solid mechanic theory. This paper analyzes full-depth reclamation (FDR), a pavement recycling rehabilitation alternative that is not modeled effectively in current design models. A random forest algorithm is calibrated based on empirical data collected from 10 FDR sites in Colorado. The resulting model yields significant improvements compared to the mechanistic-empirical model currently used in pavement design, with a reduction in RMSE of 86.7%. The study found that precipitation and freezing index have the greatest impact on rutting depth at the end of the pavement design life, with changes in traffic having a significantly lower impact on rutting. This study provides a framework for utilizing large amounts of condition, climatic, and traffic data to model pavement deterioration.
Prediction of Rutting Depth Using Random Forest Methods in Full-Depth Reclamation Asphalt Pavements
Lecture Notes in Civil Engineering
Desjardins, Serge (Herausgeber:in) / Poitras, Gérard J. (Herausgeber:in) / Nik-Bakht, Mazdak (Herausgeber:in) / Evers, Eli (Autor:in) / Shrestha, Atithi (Autor:in) / Garcia, Erick (Autor:in) / Cho, Haechad (Autor:in) / Bashar, Mohammad Z. (Autor:in) / Torres-Machi, Cristina (Autor:in) / Choi, Kunhee (Autor:in)
Canadian Society of Civil Engineering Annual Conference ; 2023 ; Moncton, NB, Canada
18.09.2024
13 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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