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.
