Efficient and Data-Driven Pavement Management System using Artificial Intelligence
PI: Emad Kassem (UI), ekassem@uidaho.edu, ORCID: 0000-0002-4331-6692
Co PIs: Billy Connor (UAF)
AMOUNT & MATCH: $180,000 from PacTrans; $180,000 Match
PERFORMANCE PERIOD: 3/16/2021 – 3/15/2022
STATUS: Completed
CATEGORIES: Pavement Management, Artificial Intelligence
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 | 10/20/2021 |
Research Project Progress Report #2 | 4/10/2022 | 4/14/2022 |
Research Project Progress Report #3 | 10/10/2022 | 10/12/2022 |
Research Project Progress Report #4 | 4/10/2023 | 4/12/2023 |
No Cost Extension Request | 1/15/2022 | 4/18/2022 |
Draft Report | 5/15/2022 | 11/2/2023 |
Final Project Report | 7/15/2022 | 12/4/2023 |