Crack propagation analysis using pavement image registration and crack vector model for predictive and precision pavement maintenance
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Yang, Zhongyu
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Abstract
Pavement maintenance is a crucial aspect of infrastructure management, impacting both the economic competitiveness of a country and the quality of life for its citizens. With over four million miles of pavement in the U.S., and more than $83 billion allocated annually for maintenance, rehabilitation, and reconstruction, there is a pressing need for cost-effective maintenance strategies. This study addresses significant gaps in predictive and precision pavement maintenance by leveraging multi-temporal pavement range images collected by 3D laser technology, aiming to achieve more cost-effective decisions in pavement maintenance and asset management. The study proposes a novel three-stage coarse-to-fine pavement image registration (PIR) methodology, an innovative crack vector model (CVM) based on the crack fundamental element (CFE) concept for flexibly storing and accurately extracting crack properties, and a comprehensive framework for the large-scale implementation of these technologies in a real-world environment. Additionally, it explores crack propagation monitoring and forecasting at both topological and aggregated levels using multi-temporal image registration and fine interval crack properties data. We assess the effectiveness of these methodologies over a 12-year dataset from a 5.8-mile section of US-80 near Savannah, GA. Results indicate that the developed methodologies significantly enhance the accuracy of pavement image registration and crack growth forecasting, supporting the implementation of predictive and precision maintenance practices following the 3R principle—right treatment, right location, right time. This approach ensures the efficient allocation of maintenance resources by precisely determining the highest priority spots for isolated, expensive treatments, such as deep patching, based on not only distress severity but also deterioration rate, to achieve the highest return on investment.
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Date
2024-07-26
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Text
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Dissertation