Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Detecting Significant Changes in Traffic Patterns for Pavement Design
The mechanistic-empirical pavement design guide (AASHTOWARE Pavement-ME) incorporates mechanistic models to estimate stresses, strains, and deformations in pavement layers using site-specific climatic, material, and traffic characteristics. These traffic characteristics include monthly adjustment factors (MAF), hourly distribution factors (HDF), vehicle class distributions (VCD), axle groups per vehicle (AGPV), and axle load distributions for different axle configurations. Site-specific traffic inputs (Level 1) were generated for each of the 41 WIM sites after extensive QC checks. The averages from nearby sites (regional) with similar traffic characteristics (groups or clusters) can be used as Level 2 data or Level 3 data when Level 1 data are unavailable. Multiple approaches were used to develop Level 2 and Level 3 traffic input levels. These developed traffic inputs at different levels need to be updated every few years due to several reasons, including the change in land use nearby the WIM locations, economic conditions resulting in the change in traffic patterns. Equations were developed to identify these changes in traffic patterns that would cause significant changes in design lives. Once these patterns are identified, the traffic inputs can be updated so that the pavement sections would not be over-designed or under-designed.
Detecting Significant Changes in Traffic Patterns for Pavement Design
The mechanistic-empirical pavement design guide (AASHTOWARE Pavement-ME) incorporates mechanistic models to estimate stresses, strains, and deformations in pavement layers using site-specific climatic, material, and traffic characteristics. These traffic characteristics include monthly adjustment factors (MAF), hourly distribution factors (HDF), vehicle class distributions (VCD), axle groups per vehicle (AGPV), and axle load distributions for different axle configurations. Site-specific traffic inputs (Level 1) were generated for each of the 41 WIM sites after extensive QC checks. The averages from nearby sites (regional) with similar traffic characteristics (groups or clusters) can be used as Level 2 data or Level 3 data when Level 1 data are unavailable. Multiple approaches were used to develop Level 2 and Level 3 traffic input levels. These developed traffic inputs at different levels need to be updated every few years due to several reasons, including the change in land use nearby the WIM locations, economic conditions resulting in the change in traffic patterns. Equations were developed to identify these changes in traffic patterns that would cause significant changes in design lives. Once these patterns are identified, the traffic inputs can be updated so that the pavement sections would not be over-designed or under-designed.
Detecting Significant Changes in Traffic Patterns for Pavement Design
Lecture Notes in Civil Engineering
Raab, Christiane (Herausgeber:in) / Musunuru, Gopi K. (Autor:in) / Haider, Syed W. (Autor:in) / Buch, Neeraj (Autor:in)
20.06.2020
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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