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基于嵌入式传感器的钢筋混凝土结构早期裂缝检测

Early Crack Detection of Reinforced Concrete Structure Using Embedded Sensors.

机构信息

NeoStrain Sp. z o.o, Lipowa 3, 30-702 Krakow, Poland.

Institute of Fundamentals of Machinery Design, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland.

出版信息

Sensors (Basel). 2019 Sep 9;19(18):3879. doi: 10.3390/s19183879.

Abstract

The damage in reinforced concrete (RC) structures can be induced either by the dynamic or static load. The inspection technologies available today have difficulty in detecting slowly progressive, locally limited damage, especially in hard-to-reach areas in the superstructure. The four-point bending test on the benchmark RC structure was used as a test of the quality and sensitivity of the embedded sensors. It allowed assessment of whether any cracking and propagation that occurs with the embedded sensors can be detected. Various methods are used for the analysis of the ultrasonic signals. By determining the feature from the ultrasonic signals, the changes in the whole structure are evaluated. The structural degradation of the RC benchmark structure was tested using various non-destructive testing methods to obtain a comprehensive decision about structural condition. It is shown that the ultrasonic sensors can detect a crack with a probability of detection of 100%, also before it is visible by the naked eye and other techniques, even if the damage is not in the direct path of the ultrasonic wave. The obtained results confirmed that early crack detection is possible using the developed methodology based on embedded and external sensors and advanced signal processing.

摘要

在钢筋混凝土(RC)结构中,损伤可以由动态或静态载荷引起。目前可用的检测技术在检测缓慢发展、局部有限的损伤方面存在困难,尤其是在结构上部难以到达的区域。基准 RC 结构的四点弯曲试验被用作嵌入式传感器质量和灵敏度的测试。它可以评估是否可以检测到嵌入传感器时发生的任何裂缝扩展。各种方法都可用于超声信号的分析。通过从超声信号中确定特征,可以评估整个结构的变化。使用各种无损检测方法对 RC 基准结构的结构退化进行了测试,以获得关于结构状况的综合决策。结果表明,即使损伤不在超声波的直接传播路径中,超声传感器也可以以 100%的概率检测到裂缝,甚至在肉眼和其他技术之前就可以检测到裂缝,即使损伤不在超声波的直接传播路径中。所得到的结果证实,使用基于嵌入式和外部传感器以及先进信号处理的开发方法可以实现早期裂缝检测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a7d/6767232/08594255a171/sensors-19-03879-g001.jpg

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