Date of Award

Fall 2021

Project Type

Dissertation

Program or Major

Civil Engineering

Degree Name

Doctor of Philosophy

First Advisor

Eshan V Dave

Second Advisor

Eshan V Dave

Third Advisor

Jo E Sias

Abstract

Efficient and effective rehabilitation of existing roadways continues to be a top priority for local, state, and federal agencies to provide safe travel of people and goods. Asphalt concrete (AC) overlays on deteriorated Portland Cement Concrete (PCC) are a popular rehabilitation option to extend the service life of a roadway. However, the combination of load and environmentally induced movements at underlying joint locations can cause high amounts of stresses and strains, leading to the formation of cracks in the AC overlay. Ensuring that a suitable asphalt mixture (cracking resistance) and sufficient overlay structure (thickness) are selected is critical to avoid pre-mature failure of overlays and excess funding requirements on pavement maintenance and rehabilitation (M&R).

This dissertation research aimed to improve the decision process of rehabilitation PCC pavements with AC overlays through the development of a Microsoft Excel®-based decision tree tool for screening of asphalt mixtures and overlay designs. A combination of laboratory testing, field performance data from full-scale pavement test sections and predicted modeling results were utilized to assess varying overlay options. The two main outputs from the decision tree tool are (1) a life cycle cost estimate and (2) predicted reflective cracking performance curves with both time and truck traffic.

Furthermore, this dissertation work sought to improve pavement life cycle assessment (LCA) and life cycle cost analysis (LCCA) practices by considering both realistic traffic conditions and future climate projections in the analysis framework. Traditional pavement LCAs are performed using historical climate data to evaluate pavement performance and provide recommendations for budgeting and planning of M&R strategies in the future. However, due to climate change, this assumption may not be appropriate as pavements’ performance is influenced by climate stressors. Research conducted as part of this dissertation showed that incorporating future project climate data and realistic traffic data can lead to a substantial increase in agency LCA impacts (up to 20% for the presented case-study), where the increase is a function of pavement structure and M&R scenario over the analysis period.

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