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INDONESIA
Civil Engineering Journal
Published by C.E.J Publishing Group
ISSN : 24763055     EISSN : 24763055     DOI : -
Core Subject : Engineering,
Civil Engineering, Building, Construction & Architecture
Civil Engineering Journal is a multidisciplinary, an open-access, internationally double-blind peer -reviewed journal concerned with all aspects of civil engineering, which include but are not necessarily restricted to: Building Materials and Structures, Coastal and Harbor Engineering, Constructions Technology, Constructions Management, Road and Bridge Engineering, Renovation of Buildings, Earthquake Engineering, Environmental Engineering, Geotechnical Engineering, Highway Engineering, Hydraulic and Hydraulic Structures, Structural Engineering, Surveying and Geo-Spatial Engineering, Transportation Engineering, Tunnel Engineering, Urban Engineering and Economy, Water Resources Engineering, Urban Drainage.
Arjuna Subject : -
Articles 1 Documents
 1 
Search results for , issue "Vol. 11 No. 5 (2025): May" : 1 Documents clear
IRI Performance Models for Flexible, Semi-Rigid and Composite Pavements in Double-Carriageway Roads Gurrutxaga, Itziar; Alonso-Solórzano, Ángela; Isasa, Miren; Pérez-Acebo, Heriberto
Civil Engineering Journal Vol. 11 No. 5 (2025): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-05-01

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 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

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