SCC-CIVIC-FA Track A: Targeted Micro-retrofits based on Building Envelope Scans using Drones, GPR, and Deep Neural Networks

SCC-CIVIC-FA 轨道 A：基于使用无人机、探地雷达和深度神经网络进行建筑包络扫描的有针对性的微改造

• 批准号: 2322242
• 负责人: Chen Feng
• 金额: $ 100万
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322242&HistoricalAwards=false
• 关键词: SCC; CIVIC; FA; Track; Targeted

Addressing a critical issue in New York City, this project targets the impending shortage of building inspection staff and the rising labor costs, especially with the implementation of Local Law 97. Traditional data collection for roof moisture reports in the building science and roofing consultancy industry is time-consuming, costly, and poses safety risks. This project’s technology offers a transformative solution that is cost-effective, labor-efficient, non-invasive, and scalable. It enables building inspection practitioners to perform more inspections than traditional methods, significantly reducing time and cost. Partnering with roofing consultants, community solar organizations, local school boards, and the municipal government, this project is particularly beneficial for groups with limited funding and large building portfolios, aiding in devising cost-effective compliance plans for climate legislation like Local Law 97. Also, the social science arm of this project investigates the barriers to the adoption of this technology and provides strategies to overcome them. The efforts will be continued after the CIVIC project through an NYU-based startup focused on commercializing the technology developed and refined throughout the project. Overall, this project increases efficiency, reduces costs, and improves safety in the building inspection process, potentially impacting the industry significantly and contributing to the broader goal of improving building maintenance and energy efficiency all over the nation.This project focuses on the development and deployment of an autonomous robotic data collection platform equipped with advanced sensors, including ground penetrating radar, LiDAR, GPS, visual cameras, and thermal cameras. It also utilizes drones to capture thermal and RGB data of building envelopes. These technologies enable the detection of moisture, thermal anomalies, and other building envelope issues without invasive procedures, minimizing the need for physical visits by engineers. The collected data is sent to cloud-based servers, where AI-powered software analyzes the information using deep learning and robot perception techniques to generate detailed reports on the moisture levels and overall condition. Combining drone-based data collection, advanced sensor technologies, and AI analysis enhances the accuracy, efficiency, and comprehensiveness of building envelope inspections. This project's scope involves piloting the technology on large roofs with complex obstructions and obstacles, particularly in New York City. This real-world testing enables system refinement and optimization for different building types and contexts. By improving building inspections with such an integrated approach, this project improves the effectiveness, cost-efficiency, and safety of the process, addressing critical building envelope issues and supporting sustainable building practices.The CIVIC Innovation Challenge is a collaboration with Department of Energy, Department of Homeland Security, and the National Science Foundation.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.

该项目旨在解决纽约市的一个关键问题，即建筑物检查人员即将短缺和劳动力成本上升，特别是随着地方法律97的实施。建筑科学和屋顶咨询行业中屋顶湿度报告的传统数据收集耗时、成本高，并存在安全风险。该项目的技术提供了一种具有成本效益、劳动效率高、非侵入性和可扩展性的变革性解决方案。它使建筑检查从业人员能够执行比传统方法更多的检查，大大减少了时间和成本。与屋顶顾问，社区太阳能组织，当地学校董事会和市政府合作，该项目对资金有限和大型建筑组合的团体特别有益，有助于为当地法律97等气候立法制定具有成本效益的合规计划。此外，该项目的社会科学部门调查了采用这项技术的障碍，并提供了克服这些障碍的战略。在CIVIC项目结束后，这些努力将通过一家总部位于纽约大学的初创公司继续进行，该公司专注于将整个项目中开发和完善的技术商业化。总体而言，该项目提高了效率，降低了成本，并提高了建筑检测过程的安全性，可能会对行业产生重大影响，并有助于实现提高全国建筑维护和能源效率的更广泛目标。该项目重点是开发和部署配备先进传感器的自主机器人数据收集平台，包括探地雷达，LiDAR，GPS，视觉摄像机和热成像摄像机它还利用无人机捕获建筑物外壳的热量和RGB数据。这些技术能够在没有侵入性程序的情况下检测水分、热异常和其他建筑围护结构问题，从而最大限度地减少工程师的实际访问需求。收集到的数据被发送到基于云的服务器，在那里，人工智能驱动的软件使用深度学习和机器人感知技术分析信息，以生成关于湿度水平和整体状况的详细报告。结合基于无人机的数据收集，先进的传感器技术和人工智能分析，提高了建筑围护结构检测的准确性，效率和全面性。该项目的范围包括在具有复杂障碍物和障碍物的大型屋顶上试验该技术，特别是在纽约市。这种真实世界的测试可以针对不同的建筑类型和环境进行系统改进和优化。通过采用这种综合方法改进建筑检查，该项目提高了过程的有效性、成本效益和安全性，解决了关键的建筑围护结构问题，并支持可持续建筑实践。CIVIC创新挑战赛是与能源部、国土安全部、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。