UTC 2022 Funding - Cycle 3 Research Projects

Project No.: CY3-LSU-03
Title: Development of Data Driven Digital Twin for Enhancing Pavement Performance Prediction in South-Central United States
Performing Institution: Louisiana State University
Principal Investigator: Mostafa Elseifi
Start and Anticipated Completion Dates: 01/01/2026 to 01/15/2027
Abstract: A comprehensive survey conducted by NCHRP Synthesis 501 revealed that many state DOTs update their pavement performance models only every 2 to 5 years, with some agencies updating even less frequently. Such lengthy update cycles mean that the models often fail to reflect recent trends in traffic loading and material performance, leading to outdated forecasts that diminish the accuracy and usefulness of maintenance and rehabilitation planning. The primary objective of this project is to develop a data[1]driven Digital Twin (DT) framework based on pavement management system data that will regularly update AI-based performance models for pavements in Louisiana. This framework aims to help state agencies make smarter, more accurate maintenance decisions while reducing costs over time. The proposed Digital Twin platform will focus on the interstate network in Louisiana, given its importance to the state and its wide implications on mobility and freight movement. The work will be divided into five tasks: (1) collect and preprocess pavement management system data for the interstate network, (2) development of digital twin framework, (3) forecast future pavement conditions in digital twin platform, (4) suggest potential maintenance strategies in the digital twin platform, and (5) prepare final report. The project will address the growing need for innovative approaches that can dynamically integrate diverse datasets, learn from both historical and emerging patterns, and provide transportation agencies with actionable, real-time insights. Digital twin technology offers this dynamic capability by enabling a shift from reactive maintenance toward predictive and proactive strategies.
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