土木结构检查用Mugraphy

Q3 Physics and Astronomy Instruments Pub Date : 2022-11-18 DOI:10.3390/instruments6040077
Subhendu Das, S. Tripathy, Priyanka Jagga, P. Bhattacharya, N. Majumdar, S. Mukhopadhyay
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引用次数: 1

摘要

老化的基础设施在全世界都是一个威胁性问题。长时间暴露在氧气和湿气中会导致钢筋混凝土结构过早腐蚀,导致结构倒塌。因此,实时监测土木结构的锈蚀情况对于避免事故发生至关重要。μ介子散射层析成像是一种非破坏性、非侵入性的技术,在民用结构的三维成像中取得了令人印象深刻的结果。本文探讨了先进的机器学习技术在使用μon散射层析成像识别锈蚀钢筋中的应用。为了实现这一点,我们模拟了成像原型装置的性能,该装置旨在进行μ介子散射层析成像,以精确测量锈蚀钢筋的锈蚀百分比。我们基于μ介子的投影3D散射顶点生成了2D图像,并使用散射顶点密度和每个像素的平均偏差角作为分析的判别参数。采用了一种滤波算法,即模式识别方法来消除背景噪声。由于这个问题归结为被分析材料是否生锈,即分类问题,我们采用了著名的机器学习算法支持向量机来识别锈蚀的钢筋水泥混凝土结构中的锈蚀。观察到,在7.3%的小误差范围内,经过训练的模型可以在标称暴露30天的情况下轻松识别出结构中30%的锈蚀。
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Muography for Inspection of Civil Structures
Aging infrastructure is a threatening issue throughout the world. Long exposure to oxygen and moisture causes premature corrosion of reinforced concrete structures leading to the collapse of the structures. As a consequence, real-time monitoring of civil structures for rust becomes critical in avoiding mishaps. Muon scattering tomography is a non-destructive, non-invasive technique which has shown impressive results in 3D imaging of civil structures. This paper explores the application of advanced machine learning techniques in identifying a rusted reinforced concrete rebar using muon scattering tomography. To achieve this, we have simulated the performance of an imaging prototype setup, designed to carry out muon scattering tomography, to precisely measure the rust percentage in a rusted rebar. We have produced a 2D image based on the projected 3D scattering vertices of the muons and used the scattering vertex density and average deviation angle per pixel as the distinguishing parameter for the analysis. A filtering algorithm, namely the Pattern Recognition Method, has been employed to eliminate background noise. Since this problem boils down to whether or not the material being analyzed is rust, i.e., a classification problem, we have adopted the well-known machine learning algorithm Support Vector Machine to identify rust in the rusted reinforced cement concrete structure. It was observed that the trained model could easily identify 30% of rust in the structure with a nominal exposure of 30 days within a small error range of 7.3%.
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来源期刊
Instruments
Instruments Physics and Astronomy-Instrumentation
CiteScore
2.60
自引率
0.00%
发文量
70
审稿时长
11 weeks
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