How do deterministic models differ from probabilistic models in pavement performance?

Deterministic models and probabilistic models differ in how they account for uncertainty in pavement performance prediction. Deterministic models provide a single, fixed prediction of pavement performance based on specific input values. These models assume that the input values are known with certainty and that the relationships between the inputs and the output are fixed and predictable. For example, a deterministic model might predict that a pavement will reach a certain distress level after a specific number of years, assuming a constant traffic volume and environmental conditions. Probabilistic models, on the other hand, explicitly account for uncertainty in the input values and the model relationships. These models provide a range of possible outcomes, along with the probability of each outcome occurring. Probabilistic models use statistical distributions to represent the uncertainty in the input values and use simulation techniques, such as Monte Carlo simulation, to generate a range of possible outcomes. For example, a probabilistic model might predict that there is a 70% probability that a pavement will reach a certain distress level within a specified timeframe, considering the uncertainty in traffic volume, environmental conditions, and material properties. Probabilistic models provide a more realistic representation of pavement performance, as they acknowledge the inherent uncertainty in the system. This allows pavement managers to make more informed decisions by considering the range of possible outcomes and the associated risks.