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Experimental research on crack damage detection of concrete beam based on PZT wave method

机译:基于PZT波法的混凝土梁裂纹损伤检测试验研究

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Concrete cracks which are gradually extended, damaged and destructed by the load have become difficult to be solved in engineering. Due to the advantages of convenient production, high sensitivity, reasonable performance-price ratio, selfsensing, piezoelectric ceramic (such as PZT) smart aggregates used as sensor and actuator are embedded in the reinforced concrete beams to generate sin-sweep excitation signals on-line and detect real-time signals with digital oscilloscope before and after damage. The optimal extraction damage signals are extracted and statistical pattern recognition algorithm of wavelet decomposition about the detection signals is established by wavelet analysis and statistical characteristics analysis. The statistical distribution of signal amplitude and the relevant damage indicators are proposed for the use of active health monitoring and energy damage principles. The results of loading tests show that the amplitude of active monitoring signal produced a larger attenuation after damage and sweep wave signals used in active health monitoring are effective in identifying the different health status of structure. The statistical pattern recognition algorithm based on wavelet packet decomposition can effectively detect crack damages of concrete structure. This technology may open a new road for active and permanent monitoring and damage detection on line as well as development of active health monitoring system based on probability statistics of piezoelectric concrete.
机译:在工程中逐渐延伸,损坏和破坏的混凝土裂缝已经难以解决。由于生产方便,高灵敏度,合理的性价比,自敏感,压电陶瓷(如PZT)智能聚集体,用作传感器和致动器嵌入钢筋混凝土梁中,在线产生SIN扫描激励信号在损坏之前和之后使用数字示波器检测实时信号。提取最佳提取损伤信号,并通过小波分析和统计特征分析建立了关于检测信号的小波分解的统计模式识别算法。提出了信号幅度和相关损坏指标的统计分布,用于使用主动健康监测和能量损坏原则。加载测试的结果表明,主动监测信号的幅度产生较大的衰减,在主动健康监测中使用的损坏和扫描波信号有效地识别结构的不同健康状态。基于小波分组分解的统计模式识别算法可以有效地检测混凝土结构的裂纹损伤。该技术可以开辟一条新的积极监测和损坏检测的新道路,以及基于压电混凝土概率统计的主动健康监测系统的开发。

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