I-Corps: Combining Traditional Building Inspection Sensors with Deep Learning and Robotics
I-Corps: Combining Traditional Building Inspection Sensors with Deep Learning and Robotics
批准号:
2232494
负责人:
Chen Feng
金额:
$5.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-08-15 至 2024-07-31
中文摘要
这个I-Corps项目的更广泛的影响/商业潜力是改善加剧温室气体排放和造成生活质量问题的建筑物围护结构。该技术旨在提供一种非侵入性的集成解决方案,以定位和记录水分入侵,热桥和空气泄漏,以诊断建筑物的围护结构问题。该系统识别和量化常见的信封缺陷,并应用长波雷达和深度学习来检测隐藏的深层水分渗透和其他主要信封缺陷。有了这个系统,就有可能进行低成本、有针对性的微改造,以解决信封问题。该项目有可能:(1)提高建筑物健康监测技术的效率和检测能力;(2)通过综合资产管理和预防性建筑物维护,提高建筑基础设施的复原力,因为早期检测能力得到提高;(3)在地方政府、行业、非营利组织之间实现强有力的交叉合作,这个I-Corps项目的基础是开发一种非侵入性的综合解决方案,用于定位和记录建筑物中的湿气侵入、热桥和空气泄漏。该系统识别和量化常见的信封缺陷,并应用长波雷达和深度学习来检测隐藏的深层水分渗透和其他主要信封缺陷。一旦确定,就有可能进行低成本、有针对性的微型改造,以解决信封问题。该项目融合了许多互补技术,这些技术有可能显著改善建筑物健康监测领域。 项目成果可以提高解决当前建筑围护结构检测问题的能力,提高广泛使用的老化商业和住宅基础设施的弹性,并为进一步研究建筑围护结构的无损检测提供基础。该奖项反映了NSF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。
英文摘要
The broader impact/commercial potential of this I-Corps project is to improve buildings envelopes that exacerbate greenhouse gas emissions and cause quality of life problems. The technology seeks to provide a non-invasive, integrated solution to locate and document moisture intrusion, thermal bridges, and air leaks to diagnose building envelope issues. The system identifies and quantifies common envelope defects and applies long-wave radar and deep learning to detect hidden deep moisture penetration and other major envelope defects. With this system, it is possible to perform low-cost, targeted micro-retrofits to address envelope issues. This project has the potential to (1) increase the efficiency and detection abilities of building health monitoring techniques; (2) increase the resilience of built infrastructure through comprehensive asset management and preventative building maintenance owing to improved early detection capabilities; (3) enable a strong cross collaboration across local government, industry, non-profits, and academia.This I-Corps project is based on the development of a non-invasive, integrated solution to locate and document moisture intrusion, thermal bridges, and air leaks in buildings. The system identifies and quantifies common envelope defects and applies long-wave radar and deep learning to detect hidden deep moisture penetration and other major envelope defects. Once identified, it may be possible to perform low-cost, targeted micro-retrofits to address the envelope issues. This project is a amalgamation of a number of complementary technologies that have the potential to significantly improve the field of building health monitoring. Project outcomes may enhance the ability to solve current building envelope inspections problems, increase the resilience of widely-used aging commercial and residential infrastructures, and provide a foundation for further study of non-destructive testing on building envelopes.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.
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