Statistical Study Based on the Kriging Method and Geographic Mapping in Rigid Pavement Defects in Southern Chile

Simple item page
datacite.alternateIdentifier.citationSUSTAINABILITY,Vol.14,,2022
datacite.alternateIdentifier.doi10.3390/su14010585
datacite.creatorMovilla-Quesada
datacite.creatorDiana Rojas-Mora
datacite.creatorJulio Raposeiras
datacite.creatorAitor C.
datacite.date2022
datacite.subject.englishPCC pavement
datacite.subject.englishdistress
datacite.subject.englishkriging
datacite.subject.englishGIS
datacite.subject.englishvariogram
datacite.titleStatistical Study Based on the Kriging Method and Geographic Mapping in Rigid Pavement Defects in Southern Chile
dc.date.accessioned2022-03-01T16:05:37Z
dc.date.available2022-03-01T16:05:37Z
dc.description.abstractASTM D6433 is used to assess the need for maintenance of pavement sections. Although the Pavement Condition Index (PCI) factor calculation method provides reliable values, this method analyzes sections and defects individually and indicates current maintenance needs, but it cannot be used to predict the occurrence of new defects. Therefore, it is necessary to complement this method by considering variables that influence the occurrence of faults, among which are the geospatial distribution and the specific characteristics of the slabs. This research focuses on the identification of multiple types of disturbances that exist in Portland Cement Pavements (PCC), located in a high traffic area in the city of Valdivia (Chile). A spatial geostatistical relationship is established through visual inspection using geographical maps, as well as distribution, using the kriging method. This technique makes use of variograms that allow quantifying the parameters used in this study, thus expressing the spatial autocorrelation of the faults analyzed. From the results obtained by spatial geostatistics and kriging, it is possible to generate a data correlation for the distribution and characteristics of the streets considered. In addition, a co-kriging method is established instead of an ordinary kriging method. The relationship between observed and predicted values improved from 0.3327 to 0.5770. The width of the slabs, as well as some streets, is shown in our analysis to be unimportant. For better model accuracy, the number of covariates associated with the type of vehicle traffic, the age and shape of the slabs, and the construction techniques used for the pavement needs to increase.
dc.identifier.urihttps://repositoriodigital.uct.cl/handle/10925/4516
dc.language.isoen
dc.publisherMDPI
dc.sourceSUSTAINABILITY
oaire.resourceTypeWOS
oaire.resourceType.enArticle
uct.indizacionSCI
uct.indizacionSSCI
Files
Collections
MigraciĆ³n Web of Science