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201864 https://hdl.handle.net/2286/R.2.N.201864 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2025 179 pages Doctoral Dissertation Academic theses en Arabi, Shiva Grau, David Eiris, Ricardo Chong, Oswald Arizona State University Partial requirement for: Ph.D., Arizona State University, 2025 Field of study: Civil, Environmental and Sustainable Engineering Infrastructure systems are the backbone of the United States economy, supporting industries, businesses, and communities by delivering essential services. Among these systems, power and water networks are especially critical due to their fundamental roles in daily life and their interdependent operations. However, much of the nation's infrastructure is aging and deteriorating, making it increasingly vulnerable to damage and structural failure. These issues often begin subtly and worsen over time, leading to higher repair costs, prolonged service disruptions, and potential risks to public safety if not addressed early. Early detection of infrastructure problems is therefore crucial to ensuring reliable service and minimizing long-term maintenance costs. Traditionally, operation and maintenance (O&M) activities have relied on manual inspections and on-site surveys. While these methods can be effective in localized areas, they are labor-intensive, time-consuming, expensive, and often impractical for monitoring the large geographical extent of infrastructure networks. Many of these networks are located remote or difficult-to-access locations, where conventional inspection approaches may fail to identify early signs of deterioration, delaying timely interventions and increasing the risk of system failures. To address these limitations, remote sensing technologies offer a promising alternative. These technologies enable rapid, large-scale data collection from both accessible and inaccessible regions, allowing for efficient water and power infrastructure O&M. This dissertation aims to explore remote sensing technologies and advanced computational methods to support water and power infrastructure O&M. Specifically, the objectives are to develop technology-enabled automated approaches and evaluate their effectiveness to: (1) automate the detection of pipe water leaks under paved surfaces, (2) automate the detection of canal water leaks, (3) evaluate the effectiveness of tree growth regulators in controlling fast-growing tree species in urban powerline corridors, and (4) assess the accuracy of remote sensing techniques in estimating tree growth parameters. The findings highlight the potential of remote sensing technologies in developing near real-time, automated infrastructure monitoring systems. By integrating remote sensing with advanced computational methods, predictive maintenance strategies can be developed to enhance efficiency, reduce operational costs, and extend the lifespan of water and power infrastructure assets. This work contributes to scalable, data-driven solutions for infrastructure monitoring and maintenance. Civil Engineering Infrastructure Monitoring Machine learning Predictive Maintenance Remote Sensing Leveraging Remote Sensing Technology and Advanced Computing Techniques to Support Operation and Maintenance of Infrastructure Systems