Improved characterization of truck traffic volumes and axle loads for mechanistic-empirical pavement design.

Details

• Creators:
  Abbas, Ala R. ; Frankhouser, Andrew
• Corporate Creators:
  University of Akron. Department of Civil Engineering
• Subject/TRT Terms:
  • Pavement design
  • Traffic data
  • Traffic surveillance
  • Automatic data collection systems
  • Weigh in motion
  • Pavement performance
  • Rigid pavements
  • Flexible pavements
  • Annual average daily traffic
  • Axle load force
  More +
• Publication/ Report Number:
  FHWA/OH-2012/21
• Resource Type:
  Tech Report
• Geographical Coverage:
  Ohio
• Edition:
  Final report.
• Corporate Publisher:
  Ohio. Dept. of Transportation. Office of Research and Development ; United States. Federal Highway Administration
• Abstract:
  The recently developed mechanistic-empirical pavement design guide (MEPDG) requires a multitude of traffic ; inputs to be defined for the design of pavement structures, including the initial two-way annual average daily truck ; traffic (AADTT), directional and lane distribution factors, vehicle class distribution, monthly adjustment factors, ; hourly truck distribution factors, traffic growth rate, axle load spectra by truck class (Class 4 to Class 13) and axle type ; (single, tandem, tridem, and quad), and number of axles per truck. Since it is not always practical to obtain sitespecific ; traffic data, the MEPDG assimilates a hierarchal level concept that allows pavements to be designed using ; statewide averages and MEPDG default values without compromising the accuracy of the pavement design. In this ; study, a Visual Basic for Application (VBA) code was developed to analyze continuous traffic monitoring data and ; generate site-specific and statewide traffic inputs. The traffic monitoring data was collected by 143 permanent traffic ; monitoring sites (93 automated vehicle classifier (AVC) and 50 weigh-in-motion (WIM) sites) distributed throughout ; the State of Ohio from 2006 to 2011. The sensitivity of the MEPDG to the various traffic inputs was evaluated using ; two baseline pavement designs, one for a new flexible pavement and one for a new rigid pavement. Key performance ; parameters for the flexible pavement included longitudinal (top-down) fatigue cracking, alligator (bottom-up) fatigue ; cracking, transverse (low-temperature) cracking, rutting, and smoothness (expressed using IRI), while key ; performance parameters for the rigid pavement included transverse cracking (% slabs cracked), joint faulting, and ; smoothness. The sensitivity analysis results revealed that flexible pavements are moderately sensitive to AADTT, ; growth rate, vehicle class distribution, and axle load spectra; and not sensitive to hourly distribution factors, monthly ; adjustment factors, and number of axles per truck. Furthermore, it was found that rigid pavements are moderately ; sensitive to AADTT, growth rate, hourly distribution factors, vehicle class distribution, and axle load spectra; and not ; sensitive to monthly adjustment factors and number of axles per truck. Therefore, it is recommended to estimate the ; AADTT and the vehicle class distribution from site-specific short-term or continuous counts and obtain the truck ; growth rate from ODOT Modeling and Forecasting Section (Certified Traffic). As for the other traffic inputs, ; statewide averages can be used for the hourly distribution factors, axle load spectra, and number of axles per truck; and ; MEPDG defaults can be used for the monthly adjustment factors.
  More +
• Format:
  PDF
• Funding:
  134557
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/25691/dot_25691_DS1.pdf
• File Type:
  [PDF - 10.89 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:161c8fa1409ae11556ea9e4a6aff90b3c1d422bccabb19e55ceecfaa428effa4f5cc156cc81975a98d9bab1dfc35e1c47279376e44ada42a2ecb6eb2262a76bf