Abstract

Abstract. Structural health monitoring has proven to be a dependable source for ensuring the integrity of the structure. It also aids in detecting and estimating the progression of cracks and the loss of structural performance. The most compelling components in the structural health monitoring system are sensing material and sensor technology. In health monitoring systems, fiber optic sensors, strain gauges, temperature sensors, shape memory alloys, and other types of sensors are commonly used. Even though the sensors bring monetary value to the system, they have some apparent drawbacks. As a result, self-sensing cement composite was established as a sensor alternative with better endurance and compatibility than sensors. Carbon nanotubes, nanofibers, graphene nanoplates, and graphene oxide are carbon-based nanomaterials with unique mechanical and electrical properties. As a result, this review comprises a complete assessment of the fresh, mechanical, and electrical properties of self-sensing cement composite developed using carbon-based nanoparticles. The research also focuses on the self-monitoring performance of cement composite in concrete beams, both bulk and embedded, by graphing the deviation of fractional change in resistivity with strain. The network channel development of carbon-based nanomaterials in cement composites and their characterization acquired using scanning electron microscopy (SEM), and X-Ray diffraction spectroscopy (XRD) research are also comprehensively discussed. According to the study, increasing carbon-based embedment decreased the relative slump and flowability while increasing the composite's compressive, split tensile, flexural, and post-peak performance. Also, the amount of carbon in the carbon-based nanomaterial directly relates to the composite's conductivity. As a result, the development of piezoresistive and sensing capabilities in carbon-based self-sensing cement composites not only improves mechanical and conductive properties but also serves as a sensor in structural health monitoring of flexural members.

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