Research

Concrete dams deteriorate during their lifespan due to continuous harsh environmental loads such as freeze-thaw, chemical attack, overload and debris impact, and natural weathering. This project aims at developing digital twin and monitoring technologies for concrete dams using computer vision, unmanned aerial vehicles, deep learning, and fiber optic sensing.

This project is inspired by NCHRP IDEA 223 and aims at developing a full-fledged AR-based bridge inspection tool that leverages CV and AI to support field detection, quantification, and documentation of various damages and deteriorations for steel bridges.

This project develops a novel Constrained-Layer Damper (CLD) for Wind-induced Vibration Mitigation of HMIPs. Unlike conventional CLDs, this new design introduces longitudinal slits into the constraining layer to effectively separate the neutral axes between the constraining layer and the base layer, drastically increase the damping performance for tubular structures.

This research introduces an innovative augmented reality (AR) application for mobile and tablet devices, designed to provide real-time visual feedback on the structural behavior of physical models. The application enhances students’ understanding of structural concepts by offering detailed and tangible insights into deflections, reactions, and the development of shear forces and moments within structural elements under applied loads.

Bridges are crucial civil infrastructure, but their deterioration over time poses significant safety risks. Traditional human visual inspections are limited in accuracy and efficiency, leading to challenges in maintaining the inventory of bridges in the United States, particularly in economically disadvantaged communities. This project aims at improving the accuracy and efficiency of bridge inspections through AI and AR, strengthening infrastructure maintenance and enhancing public safety.

This project developed a prototype strain-based fatigue crack monitoring system using a large size, high ductility, and high precision elastomeric skin-type sensor, along with wireless sensing technology to enable long-term, low cost, autonomous, and continuous fatigue crack monitoring for fracture critical bridges.

To overcome the challenges of traditional human vision inspections, this project integrated computer-vision-based motion tracking and augmented reality (AR) techniques to empower bridge inspectors to perform robust fatigue crack detection, characterization, tracking, and documentation in the field.