Chain-Structured Strain and Fracture Sensor for Bridge Structural Health Monitoring

用于桥梁结构健康监测的链结构应变和断裂传感器

• 批准号: 1301288
• 负责人: Huiming Yin
• 金额: $ 33.31万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1301288&HistoricalAwards=false
• 关键词: Chain; Structured; Strain; Fracture; Sensor

The objective of this award is to investigate the motion of ferromagnetic particles in silicone prepolymer to form a chain-structure composite for strain and fracture sensing, which will be validated in bridge structural health monitoring. In the fabrication of the sensors, ferromagnetic particles are mixed in a liquid pre-polymer, which is then cured under a high magnetic field. To improve the measurability, carbon nanotubes or graphene nanoplatelets are added to increase the conductivity of the matrix. The equivalent inclusion method will be developed to model the particle motion in the magnetic field and to predict the change of electric resistance of the sensor while it deforms with the specimen. The sensitivity and applicability of novel sensors will be validated in bridge structural health monitoring and other applications. The research activities of this project will range from nano- to macro- scales, and from the theoretical to the experimental. The success of this project will immediately result in new research areas of nanocomposites and applications in structural health monitoring and, in the longer term, improve the sustainability and safety of our infrastructure. The algorithm and modeling approach can be applied to the design of other smart materials. This sensor will exhibit large strain measurement capacity, high gauge sensitivity, integrated strain and fracture measurability, simple and prompt installation, stable performance at large temperature range, and excellent applicability to different materials. The polymer used in the new sensor can also serve as a shield for the particles, therefore it will not be susceptible to unwanted noise and can, thus, be used in some environments for which traditional strain gauges are not suitable. The equivalent inclusion method will be implemented numerically and can be applied in simulation of general hetergeneous material systems.

该奖项的目的是研究有机硅预聚物中铁磁颗粒的运动，以形成用于应变和断裂传感的链结构复合材料，该复合材料将在桥梁结构健康监测中得到验证。在传感器的制造中，铁磁颗粒混合在液体预聚物中，然后在高磁场下固化。为了提高可测量性，添加碳纳米管或石墨烯纳米片以增加基质的导电性。等效夹杂方法将被开发来模拟磁场中的粒子运动，并预测传感器的电阻的变化，而它与试样变形。 新型传感器的灵敏度和适用性将在桥梁结构健康监测和其他应用中得到验证。该项目的研究活动将从纳米到宏观尺度，从理论到实验。该项目的成功将立即导致纳米复合材料和结构健康监测应用的新研究领域，并从长远来看，提高我们基础设施的可持续性和安全性。该算法和建模方法可应用于其他智能材料的设计。该传感器具有应变测量容量大、应变灵敏度高、应变和断裂综合测量、安装简便、大温区性能稳定、对不同材料的适用性好等特点。新传感器中使用的聚合物还可以作为颗粒的屏蔽，因此它不会受到不必要的噪音的影响，因此可以用于传统应变计不适合的某些环境。等效夹杂法可用于一般非均质材料体系的数值模拟。