SBIR Phase I: Development of an artificial iIntelligence (AI)-based, internet of things (IoT)-enabled system for structural health monitoring

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响是对结构或建筑物的健康进行低成本评估。 业主和基础设施管理人员往往无法真实、公正地了解其建筑资产的实际状况。建筑物的健康状况是通过不频繁的检查或监测来评估的，这对于较小的结构来说往往是不经济的。该项目旨在开发一种能够识别结构异常的技术，以促进风险评估和适当管理。该技术旨在颠覆不断增长的结构健康监测市场，为评估建筑物和结构的当前状态提供经济实惠的解决方案，其效益成本比超过10，每个控制点的安装成本介于10美元至1，000美元之间，软件即服务（SaaS）的成本介于每月10美元至500美元之间。一旦上市，该技术可能有助于避免戏剧性的人类，环境，这一小型企业创新研究（SBIR）第一阶段项目旨在开发一个以物联网（IoT）传感器和人工智能（AI）为特色的概念验证解决方案。通过分析自然结构振动，远程自动深入了解建筑物的状况。该解决方案针对目前很少评估但存在失败风险的结构性资产。该技术利用了这样一个事实，即每个结构都有自己的自然振动特征，这取决于其设计和材料，目的和环境。可以在该振动特征中找到与退化、缺陷或故障相关的隐藏异常信号。这项新技术使用人工智能来洞察这些信号，并将其转化为有关结构健康的有价值信息。该项目旨在验证从建筑物或结构中获得的异常数据群与损坏或缺陷的关联程度，并可能有助于建立异常检测的精确度和准确度。该项目包括监测真实的建筑物或结构，制造必要的物联网设备，通过数值建模进行结构分析，使用物理模型进行实验室实验，以及使用人工智能控制危害风险。该奖项反映了NSF的法定使命，并通过使用基金会的智力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 broader impact of this Small Business Innovation Research (SBIR) Phase I project is the low-cost assessment of the health of structures or buildings. Often property-owners and infrastructure managers do not have a true and fair view of the actual state of their building assets. Building health is assessed from infrequent inspections or monitoring that is often uneconomical for smaller structures. This project seeks to develop a technology that is able to discern structural anomalies to facilitate risk assessment and proper management. This technology seeks to disrupt the growing market of structural health monitoring, providing an affordable solution for the assessment of the current state of buildings and structures with benefit to cost ratios over 10, installation costs ranging between $10 to $1,000 per control point and Software as a Service (SaaS) costs ranging between $10 and $500 per month. Once on the market, the technology may help to avoid dramatic human, environmental, and economic losses caused by damaged or collapsed buildings and structures.This Small Business Innovation Research (SBIR) Phase I project seeks to develop a proof of concept solution featuring Internet of Things (IoT) sensors and Artificial Intelligence (AI) to provide insights into the conditions of buildings remotely and automatically by analyzing natural structural vibrations. The solution targets structureal assets that currently are rarely assessed but entail risk of failure. The technology takes advantages of the fact that each structure has its own natural vibration signature, which depends on its design and materials, purpose, and environment. Hidden signals of anomalies, which can be associated with degradation, flaws, or failures, can be found encoded within this vibration signature. This novel technology uses AI to provide insight into these signals and translate them into valuable information about the health of structures. The project seeks to validate the degree to which anomalous data clusters obtained from buildings or structures can be associated with damage or defects and may help to establish the precision and accuracy in anomaly detection. The project comprises the monitoring of real buildings or structures and the manufacturing of the necessary IoT devices, the structural analyses through numerical modelling, laboratory experiments with physical models, and the use of AI to control hazard risk.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.