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

小型企业创新研究(SBIR)第一阶段项目的更广泛影响是对结构或建筑的健康状况进行低成本评估。物业业主和基础设施经理往往不能真实和公平地了解他们的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。这项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一种负担得起的解决方案，效益与成本比超过10，每个控制点的安装成本在10美元到1000美元之间，软件即服务(SaaS)成本在每月10美元到500美元之间。一旦上市，这项技术可能有助于避免损坏或倒塌的建筑和结构造成巨大的人类、环境和经济损失。这个小型企业创新研究(SBIR)第一阶段项目旨在开发一种概念验证解决方案，以物联网(IoT)传感器和人工智能(AI)为特色，通过分析自然结构振动，远程和自动地提供对建筑物状况的洞察。该解决方案针对的是结构性资产，这些资产目前很少被评估，但存在失败的风险。这项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，这取决于它的设计和材料、用途和环境。隐藏的异常信号可能与降级、缺陷或故障相关，可以在该振动信号中找到编码。这项新技术使用人工智能来提供对这些信号的洞察，并将它们转化为关于结构健康状况的有价值的信息。该项目旨在验证从建筑物或构筑物获得的异常数据群在多大程度上可以与损坏或缺陷相关联，并可能有助于确定异常检测的精确度和准确性。该项目包括对真实建筑物或结构的监控和必要物联网设备的制造，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险风险。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.