利用应用于超声波测量的孔弹性声学模型反演技术评估受腐蚀钢筋混凝土的物理特性

Pierre-Philippe Beaujean, Samuel R. Shaffer, Francisco Presuel-Moreno, Matthew Brogden
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引用次数: 0

摘要

钢筋混凝土的使用是现代基础设施的基础。有鉴于此,必须采用健康监测技术来研究这些结构内部的腐蚀情况。通过使用非破坏性方法检测钢筋混凝土内部裂缝的早期形成,本文介绍的方法旨在改进钢筋混凝土结构内部腐蚀监测的传统技术。在本文中,作者介绍了一种评估受腐蚀的钢筋混凝土物理特性的方法,该方法采用了一种孔弹性声学模型反演技术,并将其应用于一组超声波测量,这是一种新颖的方法,可用于观测腐蚀钢筋的影响以及由此造成的混凝土损坏。非接触式超声波传感器的载波频率为 500[kHz],传感器与混凝土表面之间有一层盐水隔开。在对表面和钢筋回声响应进行非接触式测量收集之后,通过改编版的 Biot-Stoll 模型,使用孔弹性模型对声音传播进行建模。首先,根据钢筋混凝土的物理特性获得一组默认参数,用于匹配样本的实验和模拟声学特征。在近九个月的时间里,对三个样本中被腐蚀的钢筋进行统计平均,发现混凝土表面与钢筋顶部之间的距离出现了微小但单调的增长,这表明钢筋在逐渐腐蚀。接下来,使用非线性优化算法来优化测量回波与模拟回波之间的匹配。通过对模型参数进行优化,测量和模拟回声之间的均方根误差在全信号和钢筋回声中分别减少了 63.7% 和 62.6%。
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Evaluation of the physical characteristics of reinforced concrete subject to corrosion using a poro-elastic acoustic model inversion technique applied to ultrasonic measurements
The use of reinforced concrete is foundational to modern infrastructure. Acknowledging this, it is imperative that health monitoring techniques be in place to study corrosion within these structures. By using a non-destructive method for detecting the early formation of cracks within reinforced concrete, the method presented in this paper seeks to improve upon traditional techniques of monitoring corrosion, within reinforced concrete structures. In this paper, the authors present a method to evaluate the physical characteristics of reinforced concrete subject to corrosion using a poro-elastic acoustic model inversion technique applied to a set of ultrasonic measurements, which constitutes a novel approach to the problem of observing the impact of corroding rebars and resulting concrete damage. A non-contact ultrasonic transducer is operated at a carrier frequency of 500 [kHz], with a layer of saltwater separating the sensor from the concrete surface. Following this non-contact measurement collection of the surface and rebar echo responses, a poro-elastic model is used to model the sound propagation, through an adapted version of the Biot-Stoll model. At first, a set of default parameters, obtained from the physical characteristics of the reinforced concrete, are used to match experimental and simulated acoustic signature of the sample. Performing statistical averaging along the corroding rebar within three samples over a period of nearly nine months, a small but monotonous increase in the distance between the concrete surface and the top of the rebar, indicating gradual corrosion of the rebar. Next, a non-linear optimization algorithm is used to optimize the match between measured and simulated echoes. Through the implementation of this model parameter optimization, the root mean square error between measured and simulated responses was reduced by 63.7% for the full signal, and 62.6% for the rebar echo.
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来源期刊
CiteScore
5.70
自引率
0.00%
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0
审稿时长
13 weeks
期刊最新文献
Automated crack identification in structures using acoustic waveforms and deep learning Inspection prioritization of gravity sanitary sewer systems using supervised machine learning algorithms Numerical investigation on the deformation of railway embankment under normal faulting Evaluation of the physical characteristics of reinforced concrete subject to corrosion using a poro-elastic acoustic model inversion technique applied to ultrasonic measurements An investigation of belief-free DRL and MCTS for inspection and maintenance planning
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