AbstractThe goal of this study is to develop a framework for the life-cycle understanding of flexible pavements. New advancements in data analytics allow for the utilization of pavement life-cycle data (historical, environmental, and structural) to evaluate the effects of material, construction, and loading parameters on the in-service performance of the pavements. In this study, the data were georeferenced to establish a connection between pavement parameters such as construction and production quality factors, traffic loading, material properties, pavement structure, and climate conditions to the long-term performance of flexible pavements. The data used in this paper were sampled from the Wisconsin Department of Transportation (WisDOT). Data were filtered to include pavement sections of comparable traffic load and environmental conditions to avoid potential bias in the analysis. Information on 42 highways with a total length of 260.5 mi was collected and analyzed for this study. Pavement deterioration metamodels were developed on high-resolution data using three machine learning (ML) techniques. For the purpose of construction of the metamodels, ML techniques including decision tree regression (DTR), random forest (RF), and gene-expression programming (GEP) were utilized by using coded subroutines in Python. The outcomes of DTR, RF, and GEP approaches showed promising results in the modeling of pavement performance by considering the effects of mix production quality factors such as air voids of the mixture (VA), individual lots voids in mineral aggregates (VMA), in-place density of asphalt mixture (%Gmm), asphalt content (AC), surface thickness, and age of pavements. This approach provides a basis for comprehensive life-cycle evaluation of the highway network without disrupting the state of practice. It relies on connecting data already being collected by the transportation agencies. The relational connection of such data allows for a pavement management system that is capable of continuously reflecting the pavement network performance on design, control, and maintenance activities.