SCC-CIVIC-PG Track A: Full Building Scans for Targeted Micro-retrofits using Drones, Radars, and Deep Learning

建筑围护结构中的空气和湿气密封不良会导致建筑物排放和居住者健康状况恶化。使用热扫描寻找水分可能是一个费力且受环境限制的过程。我们在探地雷达方面的贡献包括使用机器学习在雷达扫描中发现异常感兴趣的区域。我们将能够找到目前的无损检测技术无法检测到的被困和隐藏的水分。通过与纽约市和2030区等非政府组织的合作，该项目将：(A)研究雷达扫描对不同类型建筑立面的适用性；(B)创造真实、可行的成果，通过有针对性的微型改造改善建筑围护结构，改善纽约市贫困社区居民的生活质量；(C)创造一种有利于新建、既有建筑、建筑保险、物业管理和建筑工程行业的产品。该项目将提高美国的竞争力，生产新产品，促进经济增长，造福整个社会。这个项目的发现有可能在全国范围内扩大规模并产生类似的积极结果。维护不当的建筑围护结构会加剧建筑温室气体排放，并导致生活质量问题。我们提出了一种非侵入性的集成解决方案来定位和记录潮气入侵、热桥和空气泄漏，以诊断建筑围护结构问题。该系统识别和量化常见的围护结构缺陷，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围护结构缺陷。该项目旨在对纽约市的公共和私人建筑进行检查，特别是在低收入社区。我们将与已经开展这项工作的社区组织和地方政府部门合作，并通过PI/Co-PI实验室正在积极开发的人工智能和机器人技术来加强他们的努力。该项目将通过建造更舒适、更可用的建筑来改善低收入社区居民的生活质量。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。我们要么通过低成本、有针对性的微型翻新直接解决建筑围护结构问题，要么在不可能的情况下向我们的合作伙伴提出翻新清单。该项目是对公民创新挑战计划的响应--Track A.生活在不断变化的气候中：围绕适应、恢复和缓解的灾前行动--是NSF、国土安全部和能源部的合作。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Poor air and moisture sealing in building envelopes contribute to worse buildings emissions and occupant health outcomes. Finding moisture using thermal scans can be a laborious and environmentally constrained process. Our contribution in ground penetrating radar involves using machine learning to find anomalous areas of interest in radar scans. We will be able to find trapped and hidden moisture that is not detectable with current non destructive testing techniques. By partnering with the City of New York and NGOs like District 2030, this project will (a) research the applicability of radar scans on different types of building facades, (b) create real, actionable outcomes that will improve the quality of life for residents of disadvantaged communities in NYC by creating better buildings through improving building envelopes with targeted micro-retrofits, (c) create a product that is beneficial to the new construction, the existing construction, the building insurance, the property management, and the building engineering industries. This project will enhance US competitiveness, produce new products, bolster economic growth, and benefit society at large. The findings from this project have the potential to scale and produce similar positive outcomes across the nation.Poorly maintained building envelopes exacerbate building greenhouse gas emissions and cause quality of life problems. We propose 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. This project aims to perform inspections on public and private buildings in New York City, specifically in low-income communities. We will work with community organizations and local government departments already carrying out this work and enhance their efforts through AI and robotics technologies being actively developed in the PI/Co-PI’s labs. This project will improve the quality of life for residents in low-income communities by creating more comfortable and usable buildings. With this system, it is possible to perform low-cost, targeted micro-retrofits to address envelope issues. We will either address building envelope issues directly through low-cost targeted micro-retrofits or we will propose a list of retrofits to our partners when that is not possible. This project is in response to the Civic Innovation Challenge program—Track A. Living in a changing climate: pre-disaster action around adaptation, resilience, and mitigation—and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy.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.