Modeling the international roughness index performance on semi-rigid pavements in single carriageway roads

Pavement deterioration models are a vital feature in any pavement management system since they are capable of predicting the evolution of pavement characteristics. Pavement roughness is measured by most of the highway administrations due to its relation to comfort and safety, generally by means of the International Roughness Index (IRI). The Regional Government of Biscay (Spain) has collected IRI values since 2000 on its road network. Although many models have been developed for flexible pavements, very few have been proposed for semi-rigid pavements. The paper aims to develop IRI prediction models for semi-rigid pavements in single-carriageway roads. Considering the high quantity of available information in the database, deterministic models were selected. Due to the importance of the pavement structure in IRI evolution observed in flexible models, only segments with completely known pavement details were employed, i.e., a section where the complete structure is known: materials and thickness of existing layers above the subgrade. The pavement age, as precise as practical, and the accumulated total traffic and heavy traffic through the section were identified as roughness accelerating factors. Conversely, the materials used in base and subbase layers, their thickness, and the total thickness of bituminous layers were observed as degradation reducing factors. Possible treated base and subbase materials included in the model were soil-cement, gravel-cement, and gravel and slag. The obtained model achieved a determination coefficient (R-2) of 0.569. Additionally, the bituminous material of the surface layer was verified as an affecting factor too, which can be introduced to improve the model's accuracy. Possible surface layer materials included dense (D) and semi-dense (S) asphalt concrete, with a maximum aggregate diameter of 16 and 22 mm, discontinuous mixing (BBTM 11A) and porous asphalt (PA 11). The additional model achieved a higher determination coefficient (0.645) and, hence, a more accurate IRI prediction resulted. (C) 2020 Elsevier Ltd. All rights reserved.