It is widely acknowledged that the accurate simulation of complex engineering systems, such as nuclear power reactors, modern weapon systems, and aircraft, requires probabilistic analysis due to inherent uncertainties in their models ' parameters. The demand for and the complexity of probabilistic analysis prompted Sandia National Laboratories to develop a versatile software toolkit, DAKOTA, adaptable to various engineering applications. Pavements are another example of a complex engineering system requiring probabilistic modeling due to the uncertain nature of most of the pavement performance models parameters, including traffic, climate, material properties, and pavement structure. The deterministic pavement performance models vary from simplistic empirical relationships to complex mechanistic-empirical computational algorithms. Due to DAKOTA's independence of choice of analysis tool, it is a natural candidate to perform probabilistic aspects of pavement performance prediction. The paper presents a software framework for probabilistic modeling of pavement performance, which combines deterministic performance prediction models from the MEPDG and probabilistic analysis tools from DAKOTA. The power of this approach is demonstrated by analyzing the effect of variability in the asphalt concrete AC mix design on the variability of the pavement performance prediction.
- Probabilistic simulation
- Uncertainty analysis