Research

Integrated infrastructure monitoring using computer vision and sensing technologies

Infrastructure in rural communities such as pavements, rails, and bridges, often poorly maintained and degraded. Unlike urban areas, the rapid development of information technology and sensing technology have not benefited the infrastructure in rural communities. Integrating computer vision and advanced sensing is essential for the safety and longevity of these structures. Our research combines computer vision with acoustic and fiber optic sensing technologies for enhanced infrastructure assessment in rural areas, focusing on bridges, pavements, and rails. This approach, merging surface damage detection through computer vision with internal issue identification via sensor data, aims to improve maintenance and repair decision-making. 

Enhancing Infrastructures Resilience with Digital Twin Technology for Natural Disasters

As climate change intensifies, infrastructures face greater risks from natural disasters such as hurricanes and floods. Traditional inspection methods are often inadequate for identifying vulnerabilities before they escalate into major issues. Digital twin technology offers a cutting-edge solution by creating real-time, data-driven models that simulate infrastructures performance under various conditions. By integrating AI and machine learning, these digital twins can predict how the infrastructures will respond to future environmental challenges, enabling proactive maintenance and enhancing resilience against extreme weather events. This approach is vital for safeguarding critical infrastructure in the face of an increasingly unpredictable climate. Our goal is to establish best practices for using digital twin technology in structural assessments. By creating dynamic virtual models using real-time data, we can gain in-depth views of structural conditions. AI and machine learning will process large data sets to identify issues like delamination, corrosion, or cracking. Digital twins will evaluate infrastructure performance under disasters such as floods and hurricanes.

AI-powered Rehabilitation and Strengthening for Infrastructures

Aging structures require more than just diagnosis—traditional repair methods often fall short in addressing complex damages. Innovative solutions involving advanced materials like fiber-reinforced polymers (FRP) and AI-driven strategies are crucial for enhancing both lifespan and resilience. These cutting-edge approaches provide increased strength, corrosion resistance, and more efficient rehabilitation, ensuring structures can withstand future stresses such as heavy traffic and natural disasters. Based on accurate monitoring and digital twins, We focus on rehabilitating and strengthening damaged structures using novel materials like advanced composites and techniques such as electrochemical deposition. AI algorithms will design the most effective repair methods and strategies. This approach extends the service life of existing structures, enhances resilience to future stresses, and promotes sustainability by using durable, environmentally friendly materials.