10.18710/WD05DGJourdain, Natoya O. A. S.; Steinsland, Ingelin; Vedvik, Emil; Birkhez-Shami, Mamoona; Olsen, William; Gryteselv, Dagfin; Siebert, Doreen; Klein-Paste, AlexAlex0000-0001-6265-4438NTNU – Norwegian University of Science and TechnologyDataverseNO2024EngineeringMathematical SciencesFlexible pavementPredictive maintenanceRuttingSpatial-statistical modelUncertaintyBayesian Hierarchical modelsLinear mixed models with INLASpatial and random effectsJourdain, NatoyaNTNU – Norwegian University of Science and TechnologyNTNU – Norwegian University of Science and TechnologyAndersen, Trond MichaelTrond MichaelAndersenGryteselv, DagfinDagfinGryteselvKlein-Paste, AlexAlexKlein-PasteSiebert, DoreenDoreenSiebertIngelin SteinslandJourdain, Natoya O.A.SNatoya O.A.SJourdainNTNU – Norwegian University of Science and Technology2020-05-212024-02-142024-02-222010-05-21/2020-05-21Survey data11329138844011041458705638756244762497179501796117080238340691754330453191682437458158234059545231789348225460482text/plainapplication/zipapplication/geo+jsontype/x-r-syntaxtype/x-r-syntaxtype/x-r-syntaxtype/x-r-syntaxtype/x-r-syntaxtype/x-r-syntaxtype/x-r-syntaxapplication/x-rlang-transportapplication/x-rlang-transportapplication/x-rlang-transportapplication/x-rlang-transportapplication/x-rlang-transportapplication/x-rlang-transport1.0CC0 1.0The rutting dataset comprises of the annual rutting for years 2010-2020 (millimetre, calculated as the difference between current and previous year's data), with rut depth measurement from the previous year (millimetre), annual average daily traffic (AADT), lane width (metre), bearing capacity for year 2021 (tonnes), surface curvature index for year 2021, and base curvature index data (2021). The rutting data was collected for 20-metre road segments at specific latitude and longitude locations. The rutting is assumed to be linearly related to known explanatory variables (e.g., lane width) and random and spatial components. Rutting measurements were used to fit spatial-statistical models with random and spatial components in a Bayesian Hierarchical framework. Non spatial-statistical models with random yearly effects were also fitted. We compared these models to determine the importance of accounting for spatial information and to properly account for the rutting variability.R studio, Version: 4.3.2INLA, Version: 29-01-2024TrøndelagStatens Vegvesenhttps://www.vegvesen.no/Norwegian University of Science and Technologyhttps://www.ntnu.no/