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Calibration of CalME Models Using Field Data Collected from US 101 near Redwood National Park, Humboldt County

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  • Wu, Rongzong

Abstract

CalME is a software package under development for Caltrans that includes mechanistic-empirical response and performance models intended for the analysis and design of asphalt pavement structures. Building on existing design methods, CalME provides an Incremental-Recursive Mechanistic-Empirical (IRME) method in which the materials properties for the pavement are updated in terms of damage as the simulation of the pavement life progresses. The IRME design method incorporates various mathematical models to describe material behavior and predict structure performance. These models need to be calibrated before they can be used to evaluate the performance of different flexible pavement structures. Using data collected from two sites on US 101 near Redwood National Park in Humboldt County, California, this technical memorandum focuses on calibration of the incremental-recursive fatigue cracking model used in CalME. Fatigue shift factors of 0.075 for deterministic analysis and 0.13 for probabilistic analysis (Monte Carlo simulation) were found to best match the actual performance for the US 101 sites. These factors are quite different from those determined using accelerated pavement testing data from the WesTrack closed-circuit track and the Heavy Vehicle Simulator. More calibration studies are recommended.

Suggested Citation

  • Wu, Rongzong, 2008. "Calibration of CalME Models Using Field Data Collected from US 101 near Redwood National Park, Humboldt County," Institute of Transportation Studies, Working Paper Series qt8hx4x6kg, Institute of Transportation Studies, UC Davis.
  • Handle: RePEc:cdl:itsdav:qt8hx4x6kg
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    References listed on IDEAS

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    1. Jones, David & Fu, P. & Harvey, John T & Halles, F., 2008. "Full-Depth Pavement Reclamation with Foamed Asphalt: Final Report," Institute of Transportation Studies, Working Paper Series qt8378s3vk, Institute of Transportation Studies, UC Davis.
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