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

SCC-CIVIC-PG 轨道 A：使用无人机、雷达和深度学习进行全面建筑扫描以进行有针对性的微型改造

• 批准号: 2228568
• 负责人: Chen Feng
• 金额: $ 5万
• 依托单位: New York University
• 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
• 起止时间: 2022-10-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2228568&HistoricalAwards=false
• 关键词: SCC; CIVIC; PG; Track; Full

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.

建筑信封中的空气和水分密封不佳会导致建筑物排放和居住者健康状况较差。使用热扫描找到水分可以是实验室和环境限制的过程。我们在地面穿透力的贡献涉及使用机器学习来找到雷达扫描中感兴趣的异常领域。我们将能够找到被当前非破坏性测试技术无法检测到的被困和隐藏的水分。 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保险，物业管理和建筑工程行业。该项目将提高美国的竞争力，生产新产品，增强经济增长并使社会受益。该项目的发现有可能在全国范围内扩展和产生类似的积极成果。维护的建筑物的信封加剧了建筑温室气体排放并引起生活质量问题。我们提出了一种非侵入性集成解决方案，以定位和记录水分。入侵，热桥和空气泄漏，以诊断建筑物的包膜问题。该系统识别并量化了常见的包膜缺陷，并应用了长波雷达和深度学习，以检测隐藏的深水分穿透力和其他主要的包膜缺陷。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。我们将与已经从事这项工作的社区组织和地方政府部门合作，并通过在PI/Co-Pi实验室积极开发的AI和机器人技术来加强他们的努力。该项目将通过创建更舒适和可用的建筑物来改善低收入社区居民的生活质量。使用此系统，可以执行低成本的，有针对性的微型脱发来解决信封问题。我们要么直接通过低成本的针对性微型脱发直接解决建立信封问题，要么我们将在不可能的情况下向我们的合作伙伴提出改造列表。该项目是对公民创新挑战计划的回应 - 轨道A.生活在不断变化的气候下：围绕适应性，韧性和缓解措施的行为行动 - 是NSF，国土安全部和能源部和能源部之间的合作。该奖项反映了NSF的法定任务，并通过评估了基金会的智力效果，并被认为是对基金会的cripitiatial cripial and Intedial的支持。

项目成果

DeepGPR: Learning to Identify Moisture Defects in Building Envelope Assemblies from Ground Penetrating Radar

DeepGPR：学习通过探地雷达识别建筑围护结构组件中的潮湿缺陷

• DOI: 10.22260/isarc2023/0075
• 发表时间: 2023
• 期刊: International Symposium on Automation and Robotics in Construction (ISARC
• 作者: Sher, Bilal Ali;Feng, Chen
• 通讯作者: Feng, Chen

Marker-based Extrinsic Calibration for Thermal-RGB Camera Pair with Different Calibration Board Materials

具有不同校准板材料的热 RGB 相机对基于标记的外部校准

• DOI: 10.22260/isarc2023/0066
• 发表时间: 2023
• 期刊: International Symposium on Automation and Robotics in Construction (ISARC
• 作者: Sher, Bilal Ali;Xu, Xuchu;Chen, Guanbo;Feng, Chen
• 通讯作者: Feng, Chen