The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. The broader impact and potential societal benefit of this Convergence Accelerator Phase I project is to reduce the societal and economic impact of aging, deterioration, and extreme events on civil infrastructure by facilitating widespread monitoring and condition assessment of constructed structures. Reliable structural health monitoring tools are necessary for prioritizing maintenance and repair decisions regarding the nation’s aging infrastructure. Development of accurate, field-calibrated damage detection tools is needed to reduce the theory-to-practice gap. In turn, this project will promote the wellbeing of the community by reducing the societal and economic impact of aging, deterioration, and extreme events on civil infrastructure. The envisioned work requires diverse perspectives from multiple disciplines, and partnerships crossing organizational, institutional, and disciplinary boundaries.
This project aims to creatively integrate advances in Machine Learning (ML) and pattern recognition disciplines with physics-based reasoning to develop a novel, accurate, field-calibrated, and verified computational platform for in-situ monitoring of civil infrastructure. The main deliverable of this two-phased project is an intelligent computational platform consisting of data and algorithms for video-based damage detection and monitoring of civil engineering structures. Phase I will focus on the selection of the benchmark structures, collection of data, and the development of a prototype of the platform, which will be field calibrated in Phase II. By the end of Phase II, the project team intend to have further developed and field-calibrated the computational platform integrating the ML model with a video analytics module, which will be implemented on selected benchmark structures, and have prepared user manuals and educational materials for end-users.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
