Abstract

Health monitoring for reinforced concrete bridges and other large-scale civil infrastructure has received considerable attention in recent years. Traditional inspection methods (x-ray, C-scan etc.) are expensive and sometimes ineffective for large-scale structures. Piezoceramic transducers have emerged as new tools to health monitoring of large size structures due to the advantages of active sensing, low cost, quick response, availability in different shapes, and simplicity for implementation. In this research, piezoceramic transducers in the form of patches are used to detect internal cracks of a 6.1-meter long reinforced concrete bridge bent-cap. Piezoceramic patches are embedded in the concrete structure at pre-determined spatial locations prior to casting. This research can be considered as a continuation of an early work, where four piezoceramic patches were embedded in planar locations near one end of the bent-cap. This research involves ten piezoceramic patches embedded at spatial locations in four different cross-sections. To induce cracks in the bent-cap, the structure is subjected to loads from four hydraulic actuators with capacities of 80-ton and 100-ton. In addition to the piezoceramic sensors, strain gages, LVDTs, and microscopes are used in the experiment. During the experiment, one embedded piezoceramic patch is used as an actuator to generate sweep sinusoidal waves, and the other piezoceramic patches are used as sensors to detect the propagating waves. With the increase of number of and severity of cracks, the magnitude of the sensor output decreases. Wavelet packet analysis is used to analyze the recorded sensor signals. A damage index is formed on the basis of the wavelet packet analysis. The experimental results show that the proposed methods using piezoceramic transducers along with the damage index based on wavelet packet analysis is effective in identifying the existence and severity of cracks inside the concrete structure. The experimental results also show that the proposed method has the ability to predict the failure of concrete as verified by results from conventional microscopes (MS) and LVDTs.

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