SBIR Phase I: Prototyping Internet of Things (IoT) Sensing Platform for Infrastructure Monitoring

SBIR 第一阶段：用于基础设施监控的物联网 (IoT) 传感平台原型​​设计

• 批准号: 2139615
• 负责人: Andrew Oldiges
• 金额: $ 25.6万
• 依托单位: WAVE LOGIX, INC.
• 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: 2022-08-15 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2139615&HistoricalAwards=false
• 关键词: SBIR; Phase; Prototyping; Internet; Things

The broader impact/commercial potential of this Phase I project to enable the efficient collection, organization, maintenance, and analysis of a significant volume of reliable scientific data regarding the strength of varieties of concrete mixes from across the country and the world. It is envisions that such technology may improve the quality and safety of critical civil infrastructure, accelerate construction schedules, help optimize the performance and efficiency of construction contractors and engineers, and reduce environmental costs associated with excess cement usage. With the approval of the 2021 Infrastructure Bill, the U.S. will make the single largest investment in repairing and reconstructing the nation’s bridges and major roadways since the construction of the Interstate Highway System in the 1950s. Current concrete strength test methods are cumbersome, costly, timeinefficient, prone to error and environmentally unfriendly. Development and commercialization of a reliable, cost-effective, user-friendly Internet of Things (IoT)-based electromechanical impedance sensing system that directly measures, in real-time, in-place concrete strength without reliance on the destructive testing of concrete samples has the potential to improve the civil infrastructure industry by providing reliable data analytics. This Small Business Innovation Research (SBIR) Phase I project is aimed at determining the strength of in-place concrete, in real time, using a piezoelectric sensor and electromechanical impedance (EMI) analysis. Destructive compressive strength testing (American Society for Testing Materials (ASTM C39) and maturity testing (ASTM C1074)) are the two primary methods used to determine strength development of concrete; however, both methods rely upon the creation and destruction of concrete samples which is inefficient and unreliable as well as harmful to the environment (both due to the concrete waste caused by sampling and the excess cement usage driven by the tendency of concrete samples to show low strength). The proposed research will test a variety of concrete mixes for strength over various time points. The proposed technology requires no concrete sampling or destruction, compared to both destructive compressive strength testing and maturity testing. Successful completion of this program may result in development of a prototype of a piezoelectric concrete strength sensor and customized datalogger capable of automatic data acquisition, processing, and transmission and a customized database to directly communicate in-place concrete strength data to contractors via a secured cloud server and highly intuitive graphical user interface.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.

第一阶段项目的更广泛的影响/商业潜力，能够有效地收集，组织，维护和分析大量关于全国和世界各地各种混凝土混合物强度的可靠科学数据。 据设想，这种技术可以提高关键民用基础设施的质量和安全，加快施工进度，帮助优化建筑承包商和工程师的绩效和效率，并降低与过量使用水泥相关的环境成本。随着2021年基础设施法案的批准，美国将自20世纪50年代州际公路系统建设以来，对修复和重建全国桥梁和主要道路进行最大规模的投资。目前的混凝土强度测试方法繁琐、成本高、时间效率低、容易出错且对环境不友好。开发和商业化一种可靠的，具有成本效益的，用户友好的基于物联网（IoT）的机电阻抗传感系统，直接实时测量，现场混凝土强度，而不依赖于混凝土样品的破坏性测试，有可能通过提供可靠的数据分析来改善民用基础设施行业。该小型企业创新研究（SBIR）第一阶段项目旨在使用压电传感器和机电阻抗（EMI）分析，以真实的时间确定现场混凝土的强度。破坏性抗压强度试验美国材料试验协会（ASTM C39）和成熟度试验（ASTM C1074））是用于确定混凝土强度发展的两种主要方法;但是，在此情况下，这两种方法都依赖于混凝土样品的产生和破坏，这是低效和不可靠的，并且对环境有害。（这是由于取样造成的混凝土浪费和混凝土样品强度低的趋势导致的过量水泥使用）。拟议的研究将测试各种混凝土混合物在不同时间点的强度。 与破坏性抗压强度测试和成熟度测试相比，所提出的技术不需要混凝土取样或破坏。该计划的成功完成可能会导致压电混凝土强度传感器和定制的数据记录器的原型开发，能够自动数据采集，处理，和传输和定制的数据库，以直接在-通过安全的云服务器和高度直观的图形用户界面向承包商提供混凝土强度数据。该奖项反映了NSF的法定使命，并被认为值得支持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。