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

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Abstract

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 R2 values 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.