Integration and Deployment of Novel Tools for Rapid Assessment of Pavement Conditions and Remaining Life

Pavement condition evaluation is an essential component of a Pavement Management System (PMS) for the planning of necessary maintenance and rehabilitation activities and for preserving the road network in acceptable conditions. Timely detection and accurate quantification of pavement distresses assist PMS engineers in forecasting future pavement deterioration and planning repair strategies. Road surveying vehicles equipped with computers, sensors, cameras, and lasers are commonly used to automatically collect high-definition pavement images and have found wide acceptance by highway agencies. However, the cost of such surveys is high and cannot be afforded by many agencies, such as those responsible for city streets and rural roads. The ultimate goal of this study is to provide small to medium-sized transportation agencies that are responsible for a local road network (e.g., city roads, low-income areas, and under-served communities) with a simple tool with the ability to predict pavement condition indices, roughness, and remaining service life based on a limited set of inputs such as pavement age and classification. These inputs are commonly available to transportation agencies. This artificial intelligence (AI) and data analytics-based tool may be used in the case of the unavailability of inertial profilers and other sophisticated and expensive tools. To achieve the aforementioned goals, the following tasks will be pursued in this study: (1) Pavement performance data including pavement condition index, roughness, cracking, and rutting will be collected from the PMS databases. These data are based on pavement condition measurements that are collected biennially using a road surveying vehicle that provides a continuous assessment of the road network; (2) Artificial Neural Networks (ANN) models will then be developed to predict pavement performance parameters (e.g., roughness and pavement condition index) using simple input variables including pavement age, weather parameters, and road categories; (3) A computer-based interactive tool will be developed that can be used by transportation agencies to predict pavement performance based on simple input variables; (4) The developed interactive tool will be tested and validated based on independent performance data that were not used in the development phase. The developed tool will be available as an interactive spreadsheet or other form of computer application or phone application; (5) A final report documenting the findings of these tasks will be prepared and submitted. The research project will address the USDOT strategic goals of “Economic Strength and Global Competitiveness” and “Safety.” Developing the proposed tool will enable pavement engineers and decision-makers to select the most effective and suitable maintenance and rehabilitation strategies for maintaining the road network in adequate condition. Maintaining a well-performing and sustainable infrastructure is important to support the economy.

Language

• English

Project

• Status: Active
• Funding: $103893
• Contract Numbers:

  69A3552348306

  CY1-LSU-01

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Southern Plains Transportation Center

  University of Oklahoma
  201 Stephenson Pkwy, Suite 4200
  Norman, OK  United States  73019
• Project Managers:

  Dunn, Denise

• Performing Organizations:

  Louisiana State University, Baton Rouge

  P.O. Box 94245, Capitol Station
  Baton Rouge, LA  United States  70803
• Principal Investigators:

  Elseifi, Mostafa

  Jasim, Mahmood

• Start Date: 20231001
• Expected Completion Date: 20240930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Forecasting; Machine learning; Neural networks; Pavement management systems; Pavement performance; Roughness; Service life
• Subject Areas: Highways; Maintenance and Preservation; Pavements; Planning and Forecasting;

Filing Info

• Accession Number: 01899323
• Record Type: Research project
• Source Agency: Southern Plains Transportation Center
• Contract Numbers: 69A3552348306, CY1-LSU-01
• Files: UTC, RIP
• Created Date: Nov 15 2023 5:03PM