Sensitivity Analysis of 2002 Design Guide Rigid Pavement Distress Prediction Models
- Author(s): Kannekanti, Venkata N.;
- Harvey, John T
- et al.
The AASHTO 2002 Design Guide (2002DG) has been calibrated using Long Term Pavement Performance (LTPP) sections scattered throughout the nation but with very few sections from the state of California. This created the need to validate the models in 2002DG and recalibrate them if needed so that they may be used for pavement design and rehabilitation in California. In order to validate the design guide, a three-stage process has been identified: bench testing or sensitivity analysis, verification using accelerated pavement testing data, and verification using field data. The study presented in this report includes performing sensitivity analysis of the rigid part of 2002DG.
Sensitivity analysis helps to check the reasonableness of the model predictions, to identify problems in the software and to help understand the level of difficulty involved in obtaining the inputs. The reasonableness of the model predictions is checked by varying key design variables including traffic volume, axle load distribution, climate zone, thickness, shoulder type, joint spacing, load transfer efficiency, PCC strength, base type, and subgrade type. The chosen factorial resulted in approximately 8,500 simulations. The software outputs are transverse cracking, faulting, and IRI. A couple of related sensitivity studies have also been undertaken to study the effect of variables including surface absorptivity and coefficient of thermal expansion, which were not included in the primary sensitivity analysis.
Results from all the simulations showed that almost all of the cases produce reasonable values for transverse cracking, faulting, and IRI. The transverse cracking model is sensitive to coefficient of thermal expansion, joint spacing, shoulder type, PCC thickness, and traffic volume. The faulting values are sensitive to dowels, shoulder type, climate zone, PCC thickness and traffic volume. However, there are cases for which model predictions disagree with prevailing knowledge in pavement engineering. This study also revealed some problems associated with the software.