Yu Yilong, Dong Yulin, Jiang Yulong, Wang Fan, Zhou Qianfan, Ba Panfeng
China State Construction Hongda Engineering Co., Ltd., Beijing 100016, China.
School of Civil Engineering, Tianjin Chengjian University, Tianjin 300384, China.
Sensors (Basel). 2025 Jun 20;25(13):3844. doi: 10.3390/s25133844.
Aiming at the complex internal working conditions of steel-reinforced concrete structures, this paper proposes an active detection method for the internal hollow defects of steel-reinforced concrete based on wave analysis by using the driving and sensing functions of piezoelectric ceramic materials. The feasibility was verified through the single-condition detection test, revealing the propagation and attenuation characteristics of the stress wave signal under various detection conditions, and it was applied to the damage identification of steel-reinforced concrete rectangular section columns. Combined with the wavelet packet energy theory, the data processing of the original detection signal is carried out based on composite weighting by energy distribution entropy. Finally, the analytic hierarchy process (AHP) was introduced to study the weight vectors of different damage metrics on the detection signal, and a linear regression model based on different damage metrics was proposed as the comprehensive defect evaluation index. The research results show that the detection of internal defects in steel-reinforced concrete structures based on piezoelectric technology is applicable to concrete of different strength grades. With the increase of the detection distance and the degree of damage, the energy of the stress wave signal decreases. For example, under defect-free conditions, the energy value of the stress wave signal with a detection distance of 400 mm decreases by 92.94% compared to that with a detection distance of 100 mm. Meanwhile, it can be known from the defect detection test results of steel-reinforced concrete columns that the wavelet packet energy value under the defect condition with three obstacles decreased by 85.42% compared with the barrier-free condition, and the defect evaluation index (DI) gradually increased from 0 to 0.859. The comprehensive application of piezoelectric technology and weight analysis methods has achieved qualitative and quantitative analysis of defects, providing reference value for the maintenance and repair of steel-reinforced concrete structures.
针对钢筋混凝土结构复杂的内部工作条件，本文提出了一种基于波分析的钢筋混凝土内部空洞缺陷主动检测方法，利用压电陶瓷材料的驱动和传感功能。通过单工况检测试验验证了该方法的可行性，揭示了不同检测工况下应力波信号的传播和衰减特性，并将其应用于钢筋混凝土矩形截面柱的损伤识别。结合小波包能量理论，基于能量分布熵的复合加权对原始检测信号进行数据处理。最后，引入层次分析法（AHP）研究不同损伤指标对检测信号的权重向量，提出基于不同损伤指标的线性回归模型作为综合缺陷评价指标。研究结果表明，基于压电技术的钢筋混凝土结构内部缺陷检测适用于不同强度等级的混凝土。随着检测距离和损伤程度的增加，应力波信号的能量降低。例如，在无缺陷条件下，检测距离为400mm时应力波信号的能量值比检测距离为100mm时降低了92.94%。同时，从钢筋混凝土柱的缺陷检测试验结果可知，有三个障碍物的缺陷条件下的小波包能量值比无障碍物条件下降低了85.42%，缺陷评价指标（DI）从0逐渐增加到0.859。压电技术与权重分析方法的综合应用实现了对缺陷的定性和定量分析，为钢筋混凝土结构的维护和修复提供了参考价值。
