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基于压电陶瓷的智能骨料对混杂筋增强水泥基复合材料梁的损伤监测

Damage Monitoring of Engineered Cementitious Composite Beams Reinforced with Hybrid Bars Using Piezoceramic-Based Smart Aggregates.

作者信息

Qian Hui, Zhang Yuqing, Li Yuechang, Gao Jundong, Song Jianxue

机构信息

School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China.

出版信息

Sensors (Basel). 2022 Sep 22;22(19):7184. doi: 10.3390/s22197184.

Abstract

In order to explore the crack development mechanism and damage self-repairing capacity of ECC beams reinforced with hybrid bars, the smart aggregate-based active sensing approach were herein adopted to conduct damage monitoring of ECC beams under cyclic loading. A total of six beams, including five engineered cementitious composite (ECC) beams reinforced with different bars and one reinforcement concrete counterpart, were fabricated and tested under cyclic loading. The ultimate failure modes and hysteresis curves were obtained and discussed herein, demonstrating the multiple crack behavior and excellent ductility of ECC material. The damage of the tested beams was monitored by smart aggregate-based (SA) active sensing method, in which two SAs pasted on both beam ends were used as actuator and sensor, respectively. The time domain analysis, wavelet packet-based energy analysis and wavelet packet-based damage index analysis were performed to quantitatively evaluate the crack development. To evaluate the self-repairing capacity of the beams, a self-repairing index defined by the difference of damage index at loading and unloading peak points was proposed. The results in time domain and wavelet packed analysis were in close agreement with the observed crack development, revealing the feasibility of smart aggregate-based active sensing approach in damage detection for ECC beams. Especially, the proposed damage self-repairing index can describe the same structural re-centering phenomena with the test results, showing the proposed index can be used to evaluate the damage self-repairing capacity.

摘要

为了探究混杂配筋ECC梁的裂缝发展机理及损伤自修复能力,本文采用基于智能骨料的主动传感方法对ECC梁在循环加载下进行损伤监测。共制作了六根梁,包括五根用不同钢筋加固的工程水泥基复合材料(ECC)梁和一根对比钢筋混凝土梁,并在循环加载下进行试验。本文给出并讨论了极限破坏模式和滞回曲线,展示了ECC材料的多裂缝行为和良好的延性。通过基于智能骨料(SA)的主动传感方法对试验梁的损伤进行监测,其中粘贴在梁两端的两个SA分别用作激励器和传感器。进行了时域分析、基于小波包的能量分析和基于小波包的损伤指数分析,以定量评估裂缝发展。为了评估梁的自修复能力,提出了一个由加载和卸载峰值点损伤指数之差定义的自修复指数。时域和小波包分析结果与观察到的裂缝发展情况密切吻合,揭示了基于智能骨料的主动传感方法在ECC梁损伤检测中的可行性。特别是,所提出的损伤自修复指数能够与试验结果描述相同的结构复位现象,表明所提出的指数可用于评估损伤自修复能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2472/9572708/837effed24f0/sensors-22-07184-g001.jpg

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