Which statistical method is most commonly employed to develop pavement performance prediction models?

Regression analysis is the statistical method most commonly employed to develop pavement performance prediction models. Regression analysis is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. In the context of pavement performance, the dependent variable is typically a measure of pavement condition, such as the Pavement Condition Index (PCI) or International Roughness Index (IRI), while the independent variables are factors that influence pavement performance, such as traffic loading, environmental conditions, and pavement age. Regression analysis allows engineers to quantify the relationship between these factors and pavement condition, enabling them to predict how pavement condition will change over time. For example, a regression model might predict the rate at which rutting will develop in an asphalt pavement based on traffic volume, axle load, and temperature. Different types of regression analysis can be used, including linear regression, multiple linear regression, and non-linear regression, depending on the nature of the relationship between the variables. The goal is to find the best-fitting mathematical equation that describes the relationship between the independent variables and the dependent variable. This equation can then be used to predict future pavement performance under different scenarios.