【摘要】 目的研究混凝土梁损伤前后信号能量统计分布特征及相对损伤指标,提出一种利用压电智能传感器的混凝土结构损伤统计识别算法,实现试件损伤及破坏过程的动态监测及损伤程度评价.方法采用自感式压电智能骨料主动健康监测技术,通过埋置在混凝土梁内的一对传感器/驱动器接收发射扫频检测信号,将其作为检测样本并结合小波分析技术及其统计特征,提取最优损伤信号,结合压电波动原理,建立小波分解的混凝土结构损伤统计模式识别算法.结果混凝土梁损伤后,以扫频波作为主动健康监测信号的幅值产生了较大的衰减,试件在完全破坏后的统计损伤指数均值分别为35.9%和66.2%,利用小波分解的损伤统计模式识别算法能有效识别结构所处的健康状态.结论基于压电波动理论的主动监测技术能有效识别混凝土梁不同区域的损伤状态,为压电混凝土结构动态、长期监测裂缝开展状态与损伤统计识别技术及其系统的研发提供新的思路.更多还原

【Abstract】 This paper proposes a statistical pattern identification algorithm of wavelet decomposition,which is established by wavelet analysis and statistical characteristics analysis and then extracting optimal damage signals.The statistical distribution of signal amplitude and the relative damage index are obtained for the use of active health monitoring and PZT wave theory.Due to advantages of convenient in production,high sensitivity,reasonable performance-price ratio,self-sensing piezoelectric ceramic(PZT)smart aggregates as sensor and actuator are embedded in reinforced concrete beams with three-point bending to generate sin-sweep excitation signals on-line and detect real-time signals with digital oscilloscope before and after damage based on PZT active health monitoring.Tests show that the amplitude of active monitoring signal produced a larger attenuation and the statistical average of the damage index after damage of beams is 35.9% and 66.2% respectively and sweep wave signals as active health monitoring are an effective method to identify the different health status of structure.Conclusively,the statistical pattern identification algorithm based on wavelet decomposition can effectively identify damage of concrete structure.This technology opens a new road for active and permanent monitoring and damage detection on line and carrying out active health monitoring system based on probability statistics of PZT smart concrete structures.更多还原