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弹性波在含GFRP增强材料的混凝土板损伤检测中的应用

Elastic Wave Application for Damage Detection in Concrete Slab with GFRP Reinforcement.

作者信息

Ziaja Dominika, Jurek Michał, Wiater Agnieszka

机构信息

Department of Structural Mechanics, Rzeszow University of Technology, ul. Poznańska 2, 35-084 Rzeszów, Poland.

Department of Roads and Bridges, Rzeszow University of Technology, ul. Poznańska 2, 35-084 Rzeszów, Poland.

出版信息

Materials (Basel). 2022 Nov 29;15(23):8523. doi: 10.3390/ma15238523.

Abstract

The aim of the presented examination is condition-monitoring of GFRP-reinforced concrete structural members using elastic wave propagation. As an example, a deck slab is selected. The deck slab is made of concrete of the targeted C30/37 class under three-point bending. During loading cycles, the specimen is observed with a digital image correlation (DIC) system, which enables calculation of the strain field. The measuring setup consists of two Baumer 12.3 Mpx cameras with VS-1220HV lenses, combined in a Q400 system by Dantec Dynamics GmbH. Elastic waves are also measured based on signals recorded with PZT (lead-zirconate-titanate) sensors. Additionally, the typical crack-opening measurements are made. The appearance of a crack and its growth causes changes in both the shape and amplitude of the registered signals. However, the changes are not obvious and depend on the location of the sensors. Due to the impossibility of determining simple parameters with respect to disturbingly wide cracks, for damage detection, an artificial neural network (ANN) is applied. Perfect determination of the specimen's condition (100% properly classified patterns) is possible based on whether the element is under loading or not.

摘要

本文所介绍的检测目的是利用弹性波传播对玻璃纤维增强塑料(GFRP)增强混凝土结构构件进行状态监测。以一块桥面板为例。该桥面板由目标强度等级为C30/37的混凝土制成,承受三点弯曲。在加载循环过程中,使用数字图像相关(DIC)系统对试件进行观测,该系统能够计算应变场。测量装置由两台配备VS - 1220HV镜头的堡盟1230万像素相机组成,由丹泰克动力有限公司组合在一个Q400系统中。还基于用压电陶瓷(PZT,锆钛酸铅)传感器记录的信号来测量弹性波。此外,还进行了典型的裂缝开口测量。裂缝的出现及其扩展会导致所记录信号的形状和幅度发生变化。然而,这些变化并不明显,并且取决于传感器的位置。由于对于宽度令人困扰的裂缝无法确定简单参数,因此为了进行损伤检测,应用了人工神经网络(ANN)。根据构件是否处于加载状态,可以完美地确定试件的状态(100%正确分类的模式)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df93/9741480/908ea91b4323/materials-15-08523-g001.jpg

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