IRI Performance Models for Flexible, Semi-Rigid and Composite Pavements in Double-Carriageway Roads

Pavement Management Systems (PMS) depend upon reliable pavement performance models. In this paper, our aim is to develop International Roughness Index (IRI) prediction models for the heavily trafficked (right-hand) lanes of motorways in the province of Gipuzkoa (Spain) in flexible, semi-rigid, and composite pavements. A deterministic approach was selected, based on the available information in the PMS employed in that province, covering complete pavement structures. Omitting pavement type, the model yielded a determination coefficient (R²) of 0.696 with only three variables: pavement age, cumulative volume of heavy vehicles travelling through the section, and total thickness of bituminous layers. Then, two superior models were generated with pavement type as a variable, yielding R²values of 0.781 and 0.795, respectively. Unlike the opaque features of Machine Learning (ML), the deterministic models captured precise relationships between the variables to a high degree of accuracy. They can moreover be applied to all pavements with bituminous layers, unlike many other models that are only applicable to a single pavement type. Furthermore, the models are presented for freeways where traffic is randomly distributed between lanes; a less widely covered topic in the literature. Doi: 10.28991/CEJ-2025-011-05-01 Full Text: PDF