Smart self-sensory carbon-based textile reinforced concrete structures for structural health monitoring

Abstract

The goal of this study is to develop a structural health monitoring methodology for smart self-sensory carbon-based textile reinforced concrete elements. The self-sensory concept is based on measuring the electrical resistance change in the carbon roving reinforcement and by means of an engineering gage factor, correlating the relative electrical resistance change to an integral value of strain along the location of the roving. The concept of the nonlinear engineering gage factor that captures the unique micro-structural mechanism of the roving within the concrete matrix is demonstrated and validated. The estimated value of strain is compared to a theoretical value calculated by assuming a healthy state. The amount of discrepancy between the two strain values makes it possible to indicate and distinguish between the structural states. The study experimentally demonstrates the engineering gage factor concept and the structural health monitoring procedure by mechanically loading two textile reinforced concrete beams, one by a monotonic loading procedure and the other by a cyclic loading procedure. It is presented that the proposed structural health monitoring procedure succeeded in estimating the strain and in clearly distinguishing between the structural states.