A GIS-based early warning tool for pavement deterioration due to unusually heavy truck loads

dc.contributor.advisorMachemehl, Randy B.
dc.creatorLi, Tianxin, M.S. in Engineering
dc.date.accessioned2018-02-07T20:44:54Z
dc.date.available2018-02-07T20:44:54Z
dc.date.created2017-12
dc.date.issued2018-01-25
dc.date.submittedDecember 2017
dc.date.updated2018-02-07T20:44:55Z
dc.description.abstractBudget from transportation agencies cannot meet the increasing requirements of pavement maintenance. Preventative pavement maintenance is accepted as a more cost-effective way to keep pavement in an acceptable level with less investment, compared to the reactive pavement maintenance. Traffic information, especially that about heavy truck trips, can help agencies to determine when and how to take preventative pavement maintenance. Two types of unusually heavy truck trips, associated with energy extraction (oil and gas) and urban development activities, were analyzed in this study. Their equivalent single axile loads (ESALs) were then mapped to the network. In addition, existing pavement condition, measuring by ride score, one of the common indexes of Pavement Serviceability, was considered into the model. A new index, ESALs divided by ride score, was introduced to reflect the priority of pavement maintenance of the network influenced by the unusually heavy truck trips. A GIS-Based model, automating the process of analysis by employing the Python Toolbox and online ArcGIS Add-in, was developed. Results include the maps and Excel tables to help agencies understand the priority of pavement maintenance of the network in terms of ESALs and current pavement condition.
dc.description.departmentCivil, Architectural, and Environmental Engineering
dc.format.mimetypeapplication/pdf
dc.identifierdoi:10.15781/T2V40KG3Z
dc.identifier.urihttp://hdl.handle.net/2152/63562
dc.language.isoen
dc.subjectGIS
dc.subjectESALs
dc.subjectEquivalent single axile loads
dc.subjectPavement
dc.subjectHeavy trucks
dc.subjectPavement deterioration
dc.subjectGIS-based tools
dc.titleA GIS-based early warning tool for pavement deterioration due to unusually heavy truck loads
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentCivil, Architectural, and Environmental Engineering
thesis.degree.disciplineCivil Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Engineering
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