Efficient and Data-Driven Pavement Management System using Artificial Intelligence

PI: Billy Connor (UAF), bgconnor@alaska.edu, ORCID: 0000-0002-4289-2620

Co PIs: Emad Kassem (UI)

AMOUNT & MATCH: $180,000 from PacTrans; $180,000 Match

PERFORMANCE PERIOD: 3/16/2021 – 3/15/2022

STATUS: Active

CATEGORIES: Pavement Management, Artificial Intelligence

RESEARCH PROJECT HOT SHEET:

UTC PROJECT DOCUMENTATION:

FINAL PROJECT REPORT: will be available once completed

PROJECT DATA: will be available once completed

DESCRIPTION: Pavement management systems are used by transportation agencies to assist pavement engineers to determine cost-effective strategies for pavement preservation and maintenance at the network level. A large amount of data is collected every year as part of the pavement management program. Such data include road location, geometry, roughness, cracking, rutting, texture, skid resistance, traffic level, pavement structure, material properties, and others. This information is processed using traditional analytical-based methods to predict future pavement conditions and program pavement preservation and rehabilitation treatments at the network level.

The traditional analytical-based tools used in the pavement management systems do not use the complete information instead they focus on one aspect of the data (e.g., surface distresses or skid condition). Nevertheless, due to the increasing complexity and scale level of collected data, the current methods may not be able to provide an accurate pavement condition assessment and optimal preservation/rehabilitation treatments. Recently, Artificial Intelligence (AI) has been used, as a powerful tool, to examine large data sets that often very challenging to be analyzed by traditional methods and derive helpful correlations and models. Such models can be used to assist scientists and engineers in making informed decisions.

DELIVERABLE DUE DATE DATE RECEIVED
Research Project Progress Report #1 10/10/2021
No Cost Extension Request 1/15/2022
Draft Report 1/15/2022
Final Project Report 3/15/2022