10.18710/WD05DG Jourdain, Natoya O. A. S.; Steinsland, Ingelin; Vedvik, Emil; Birkhez-Shami, Mamoona; Olsen, William; Gryteselv, Dagfin; Siebert, Doreen; Klein-Paste, Alex Alex https://orcid.org/0000-0001-6265-4438 NTNU – Norwegian University of Science and Technology DataverseNO 2024 Engineering Mathematical Sciences Flexible pavement Predictive maintenance Rutting Spatial-statistical model Uncertainty Bayesian Hierarchical models Linear mixed models with INLA Spatial and random effects NTNU – Norwegian University of Science and Technology NTNU – Norwegian University of Science and Technology Jourdain, Natoya Natoya Jourdain NTNU – Norwegian University of Science and Technology Andersen, Trond Michael Trond Michael Andersen Gryteselv, Dagfin Dagfin Gryteselv Klein-Paste, Alex Alex Klein-Paste Siebert, Doreen Doreen Siebert Ingelin Steinsland Jourdain, Natoya O.A.S Natoya O.A.S Jourdain 2020-05-21 2024-02-14 2024-02-22 2010-05-21/2020-05-21 2010-05-21/2020-05-21 Survey data https://doi.org/10.48550/arXiv.2401.03633 10.18710/WD05DG/KWYWPJ 10.18710/WD05DG/AQTRJL 10.18710/WD05DG/P7XQ7V 10.18710/WD05DG/JS4B47 10.18710/WD05DG/GHF2CJ 10.18710/WD05DG/CRHMPA 10.18710/WD05DG/R7J5CT 10.18710/WD05DG/UF67NM 10.18710/WD05DG/UEYBDM 10.18710/WD05DG/QHMIGD 10.18710/WD05DG/CSNOJA 10.18710/WD05DG/3GMBIQ 10.18710/WD05DG/3VWXVL 10.18710/WD05DG/APCJMF 10.18710/WD05DG/E4ANEY 10.18710/WD05DG/8LWXUK 1041458705 238340 63875 17961 17080 17950 62447 62497 231789348 225460482 234059545 691754330 453191682 437458158 11329 13884401 application/geo+json type/x-r-syntax type/x-r-syntax type/x-r-syntax type/x-r-syntax type/x-r-syntax type/x-r-syntax type/x-r-syntax application/x-rlang-transport application/x-rlang-transport application/x-rlang-transport application/x-rlang-transport application/x-rlang-transport application/x-rlang-transport text/plain application/zip 1.0 Creative Commons CC0 1.0 Universal Public Domain Dedication. The 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: 4.3.2 INLA, Version: 29-01-2024 Norway, Trøndelag,, Trondheim to Storlien on the Swedish border, Trøndelag Statens Vegvesen https://www.vegvesen.no/ Norwegian University of Science and Technology https://www.ntnu.no/