Textile reinforced concrete (TRC) technology enables to construct sustainable and environmentally friendly thin-walled structural elements with self-sensory capabilities. The self-sensory concept is based on using high-strength and electrically conductive yarns that are simultaneously used as the main reinforcement system and as the sensory agent. By connecting the two ends of the yarns to either direct current (DC) or alternating current (AC) electrical circuits, it is possible to measure changes in the electrical readings and to further correlate them to the structural health. Studies in the literature validated the concept by using carbon yarns as hybrid monitoring agents. Most of them provided integrated assessments of global structural health but lacked the capability to pinpoint individual cracks. The current study offers a new identification procedure to locate cracks and to estimate their severity by combining two advanced monitoring procedures, that is by adopting the Time Domain Reflectometer (TDR) analysis; and by conceptually using the smart water leakage detection method. Both methods take advantage that a pair of carbon yarns can be positioned parallel to each other and connected to the DAQ system. In case of TDR method, one yarn functions as the signal transmitter and the other as insulation. In case of water leakage detection method, the electrical linkage between the two yarns changes the electrical characterization of the electrical circuit and enables the monitoring capabilities. The proposed study argues that a new identification procedure that benefits from each method can be developed and yields to smart identification procedure that has the capability to locate the crack and estimate its severity. The study will demonstrate the feasibility of the new procedure by experimental study. Results from this study will take a significant step toward developing reliable smart carbon-based TRC structures.
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