Implementation and Improvement of Pavement & Asset Management Systems in Tennessee
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2016-10-01
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Edition:Interim Report (Final) July 2013 to October 2016
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Abstract:This project aims to assist in implementation of PMS including data collection, condition report and strategy analysis and help developing the asset management plan for the state. The research team accomplished several proposed tasks including: defining the most recent highway information and pavement maintenance treatments in PMS; examining “untreated” and “poor” pavement segments; updating pavement related data; generating annual pavement condition report; producing the MAP21 pavement condition report for the FHWA; conducting pavement maintenance strategy analysis. In addition to those routine work, the research team also worked on several tasks for pavement maintenance and management, including: 1. All the poor pavement sections in Tennessee were examined by investigating the pavement condition data in the PMS and visual inspection using the TRIMs. The causes of those poor sections were analyzed. By setting the roughness, rutting and distress thresholds, poor pavement segments in Tennessee were identified. Related traffic and maintenance information of those pavement segments were also collected. 2. The optimized sampling method for pavement inspection for Tennessee were analyzed based on TDOT’s inspection protocol and historical passing percent. 3. Failure probability of typical pavement preventive maintenance treatments were analyzed using survival analysis. The pretreatment performance level was identified as the most significant factor influencing treatment performance, followed by treatment type, traffic level and environmental factors. 4. To calibrate the weights of single pavement distress indicator and the overall pavement distress index (PDI), the structural equation modeling method was utilized to find the correlations between different pavement condition indices. 5. The effects of construction considerations on performance maintenance treatments were analyzed by data mining technology. To overcome the difficulties in those factor analysis, the decision tree method was adopted to identify and quantify the influence of those factors.
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