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Integration Of Geographic Information System (gis) And Paver System Toward Efficient Pavement Maintenance Management System (pmms)

Submitted2018-05-14
Last Update2018-05-14
TitleIntegration Of Geographic Information System (gis) And Paver System Toward Efficient Pavement Maintenance Management System (pmms)
Author(s)Author #1
Author title:
Name: Mohammed Taleb Obaidat
Org: Professor of Civil Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan
Country:
Email: mobaidat@just.edu.just

Author #2
Author title:
Name: Khalid A. Ghuzlan
Org: Associate Professor of Civil Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110 Jordan
Country:
Email: kaghuzlan@just.edu.jo

Author #3
Author title:
Name: Bara' W. Al-Mistarehi
Org: Assistant Professor of Civil Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110 Jordan
Country:
Email: bwmistarehi@just.edu.jo

Other Author(s)
Contact AuthorAuthor #3
Alt Email: bwmistarehi@just.edu.jo
Telephone:
KeywordsGIS, PMMS, PCI, Pavement distresses, Pavement conditions, Distress classification, Maintenance priorities
AbstractThe main objective of this research work was to investigate the potential of integration of Geographic Information System (GIS) and PAVER system for the purpose of flexible pavement distress classification and maintenance priorities. Classification process included distress type, distress severity level and options for repair. A system scheme that integrated the above-mentioned systems was developed. The system utilized the data collected by PAVER system in a GIS environment. GIS ArcGIS software was used for the purpose of data display, query, manipulation and analysis. The developed system was of great help in identifying, collecting and displaying pavement condition data. Pavement distresses were assigned based on pavement condition index values computed by Pavement Condition Index (PCI). This technique was cost-effective and appropriate for wise decision making for different maintenance activities and programs. Statistical models were developed to forecast pavement distress quantities using Average Daily Traffic (ADT), climate conditions, socio-economical characteristics and pavement age. ADT and pavement age variables were the most significant factors in distress quantification.
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