Statistical algorithm for damage detection of concrete beams based on piezoelectric smart aggregate

Abstract

The functional piezoelectric ceramic smart aggregate (SA) sensors and actuators, based on piezoelectric ceramic materials such as lead zirconium titanate (PZT), were embedded into the reinforced concrete beams with three-point bending under static loading for purposes of damage detection. The SA actuators generated the desired sine sweep excitation signals online and the SA sensors received and detected real-time signals before and after damage. The wavelet analysis and statistical characteristics about damage signals were used as a signal processing and analysis tool to extract the optimal damage information and establish a statistical damage detection algorithm. The damage index-based wavelet analysis and damage probability-based probability and statistics were proposed by PZT wave-based theory and active health monitoring technology. The results showed that the existence of cracks inside largely attenuated the amplitude of active monitoring signal after the damage of beam and the attenuation was related to the severity degree of damage. The innovative statistical algorithm of damage pattern detection based PZT-SA can effectively determine damage probability and damage degree, and provide a prediction for the critical damage location of reinforced concrete structures. The developed method can be utilized for the structural health comprehensive monitoring and damage detection on line of various large-scale concrete structures.