Increased demand for efficient maintenance of the existing roadway system needs optimal usage of the allocated funds. The paper demonstrates optimized methods for prioritizing maintenance implementation projects. A selected zone of roadway system in Kerbala city represents the study area to demonstrate the application of the developed prioritization process. Paver system PAVER integrated with GIS is used to estimate and display the pavement condition index PCI, thereby to establish a priority of maintenance. In addition to simple ranking method by PCI produced by the output of PAVER, the paper introduces PCI measure for each section of roadway. The paper introduces ranking by multiple measures investigated through expert knowledge about measures that affect prioritization and their irrespective weights due to a predesigned questionnaire. The maintenance priority index (MPI) is related to cost of suitable proposed maintenance, easiness of proposed maintenance, average daily traffic and functional classification of the roadway in addition to PCI. Further, incremental benefit-cost analysis ranking provide an optimized process due to benefit and cost of maintenance. The paper introduces efficient display of layout and ranking for the selected zone of roadway system based on MPI index and incremental BCR method. Although the two developed methods introduce different layout display for priority, statistical test shows that no significant difference between ranking of all methods of prioritization.
Pavement Condition Index (PCI) is one of the important basics in pavement maintenance management system (PMMS), and it is used to evaluate the current and future pavement condition. This importantance in decision making to limit the maintenance needs, types of treatment, and maintenance priority. The aim of this research is to estimate the PCI value for flexible pavement urban roads in the study area (kerbala city) by using Garber et al. developed model.Based on previous researches, data are collected for variables that have a significant impact on pavement condition. Data for pavement age (AGE), average daily traffic (ADT), and structural number (SN) were collected for 44 sections in the network roads. A field survey (destructive test (core test) and laboratory test (Marshall Test)) were used to determine the capacity of structure layer of pavement (SN). The condition index (CI) output from a developed model was compared with the PCI output of PAVER 6.5.7 by using statistical analysis test.The developed model overestimates value of CI rather than PCI estimated from PAVER 6.5.7 due to statistical test to a 95% degree of confidence, (R = 0.771) for 44 sections (arterial and collector).
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