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600451-00052

SWUTC Ph.D. Candidate Assistantship Project Description

Integration of Heuristics and Statistics to Improve the Quality of Network-Level Pavement Condition Data

University: Texas A&M University

Principal Investigator:
Salar Zabihi Siabil
Zachry Department of Civil Engineering

Faculty Supervisor:
Dr. Nasir Gharaibeh
Zachry Department of Civil Engineering
(979) 845-3362

Funding Source: USDOT Funds

Total Project Cost: $37,592

Project Number: 600451-00052

Date Started: 10/1/15

Estimated Completion Date: 8/31/15

Project Summary

Project Abstract:
Improving the quality of pavement management data is a major challenge facing transportation agencies. The quality of this data can affect not only the assessment of current and predicted future condition of the network, but also the quality of decisions regarding pavement maintenance and rehabilitation (M&R) activities. This research aims to devise and implements a computational method to identify potential errors in pavement condition data. The research questions are: (a) How can we identify potential errors in pavement condition data used in Pavement Management Systems (PMSs)?, and (b) how does accuracy of pavement condition data impact the predictions of future performance of the road network? The specific objectives are (a) develop a computational technique to detect potential errors in pavement condition datasets, (b) validate the developed error detection technique using real world data, and (c) assess the impact of accuracy in pavement condition data on predictions of future performance of the road network.

Project Objectives:
The aim of this research is to find answers to the questions raised below and develop a computational technique for identifying potential errors in network-level pavement condition datasets.

  • How can we identify potential errors in pavement condition data used in PMSs?
  • How does accuracy of pavement condition data impact the predictions of future performance of the road network (a key capability of PMSs)?

Task Descriptions:

Task 1:
Establish pavement performance families.

Task 2:
Detect statistical outliers within each pavement family for each pavement condition indicators.

Task 3:
Integrate outlier detection method and heuristic-based consistency checks to identify potential errors in pavement condition data.

Task 4:
Test and validate the developed error detection method using a real world pavement condition data from Texas.

Task 5:
Compare the remaining service life (RSL) of the road network based on two scenarios: original database and clean database.