Sensitivity of the mechanistic-empirical pavement design guide to traffic inputs: a space-filling approach

摘要

The mechanistic-empirical pavement design guide (MEPDG) includes a software tool to perform the large number of calculations involved in the design of pavement structures. Large number of input parameters from traffic, materials, and climate are required to use MEPDG. Data collection to accomplish calibration and implementation of MEPDG may be done more efficiently focusing on the most sensitive parameters. This research studies the sensitivity of MEPDG to traffic input parameters on two flexible pavements using the techniques for sensitivity of complex computer codes with Latin hypercube sampling, rank transformation, standardised regression coefficients, and Gaussian stochastic models. The results showed that roughness, rutting, and bottom-up cracking as predicted by MEPDG are sensitive to variations of two-way average annual daily traffic (AADT), traffic growth, dual tyre spacing, percent trucks in the design direction, percent of heavy vehicles, traffic wander, and percent of trucks in design lane. Variations of operational speed, average axle width, and mean wheel location showed no significant effects on the same MEPDG outputs. The results of the sensitivity were used to define recommended hierarchical levels for traffic input.