NSF Convergence Accelerator Track D: Intelligent Surveillance Platform for Damage Detection and Localization of Civil Infrastructure

NSF融合加速器支持以使用为灵感，以团队为基础，多学科的努力，解决国家重要性的挑战，并将在不久的将来产生对社会有价值的可交付成果。“融合加速器”一期项目的更广泛影响和潜在社会效益是，通过促进对已建成结构的广泛监测和状况评估，减少老化、退化和极端事件对民用基础设施的社会和经济影响。可靠的结构健康监测工具对于国家老化基础设施的优先维护和维修决策是必要的。需要开发精确的、现场校准的损伤检测工具，以缩小理论与实践的差距。反过来，该项目将通过减少老化、老化和极端事件对民用基础设施的社会和经济影响来促进社区的福祉。设想的工作需要来自多个学科的不同观点，以及跨越组织、机构和学科边界的伙伴关系。该项目旨在创造性地将机器学习（ML）和模式识别学科的进展与基于物理的推理结合起来，开发一种新颖、准确、现场校准和验证的计算平台，用于民用基础设施的现场监测。这个分两阶段的项目的主要成果是一个智能计算平台，由数据和算法组成，用于基于视频的土木工程结构损伤检测和监测。第一阶段将侧重于基准结构的选择、数据的收集和平台原型的开发，该原型将在第二阶段进行现场校准。在第二阶段结束时，项目团队打算进一步开发和现场校准集成ML模型和视频分析模块的计算平台，该平台将在选定的基准结构上实施，并为最终用户准备用户手册和教育材料。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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 thebenchmark 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.