Surface and Subsurface Remote Sensing of Concrete Structures Using Synthetic Aperture Radar Imaging

Yu, Tzuyang; Twumasi, Jones Owusu; Le, Viet; Tang, Qixiang; D'Amico, Nicolas

doi:10.1061/(asce)st.1943-541x.0001730

Cited by 20 publications

(5 citation statements)

References 18 publications

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“…SAR imaging is capable of producing surface and subsurface profiles for dielectric targets like concrete (or Portland cement concrete). With several imaging modes (spotlight, stripmap, and inverse), 20 flexible resolutions (synthetic aperture) and options for bandwidth enhancement, SAR imaging can be used to interrogate concrete for its material properties such as the moisture content, [21][22][23] water-to-cement ratio, 24 chloride content, 25,26 subsurface steel rebar location, 27 damage and delamination detection, 28 and evaluation of masonry materials. 20 The fundamental principle of SAR imaging for subsurface sensing in concrete is EM backscattering in dielectric media.…”

Section: Introductionmentioning

confidence: 99%

Corrosion detection of steel-reinforced concrete specimens using synthetic aperture radar

Raisi,

Abazarsa,

2024

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII

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Chloride-induced corrosion attack in steel-reinforced concrete highway bridges is a common and ongoing issue in New England. If left untreated, late-stage corrosion can result in steel rebar section loss, internal stress imbalance, and surface cracks. In recent years, nondestructive testing and evaluation (NDT/E) techniques have been emerging as alternative methods for structural health monitoring (SHM) of civil infrastructure. This paper aims to use synthetic aperture radar (SAR) for corrosion detection of steel-reinforced concrete (RC) panels that were subjected to chloride-induced rebar corrosion. For this purpose, three RC panels (30 × 30 × 12.7 cm 2 ) were cast with a No.6 steel rebar (19 mm diameter) at their mid-height with one serving as baseline, and the other two were corroded by accelerated corrosion test (ACT). The RC panels were kept in a temperature-controlled environment (23 − 25 • C) since 2017. The RC panels were scanned by a laboratory 10.5 GHz SAR with a 1.5 GHz bandwidth to develop SAR images with two scan ranges of 60 and 70 centimeters. The SAR images were analyzed in time domain and their amplitude parameters were used for corrosion detection. Furthermore, a half-cell potential device was used for verification and quantification in conjunction with out SAR parameters. Our results indicate that the progression of corrosion based on HCP, is correlated with SAR signal parameters such as maximum and integrated, and average SAR amplitudes.

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Section: Introductionmentioning

confidence: 99%

Corrosion detection of steel-reinforced concrete specimens using synthetic aperture radar

Raisi,

Abazarsa,

2024

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII

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“…Therefore, it is of great significance to detect and monitor the health of concrete structures. At present, the concrete crack detection methods mainly include the radar method [ 1 ], moiré method [ 2 ], infrared thermography method [ 3 ], acoustic emission method [ 4 ], and holographic interference method [ 5 ]. However, these methods are mainly based on contact or embedded methods to detect cracks, and are greatly affected by the external environment such as temperature and humidity.…”

Section: Introductionmentioning

confidence: 99%

Crack Texture Feature Identification of Fiber Reinforced Concrete Based on Deep Learning

et al. 2022

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Structural cracks in concrete have a significant influence on structural safety, so it is necessary to detect and monitor concrete cracks. Deep learning is a powerful tool for detecting cracks in concrete structures. However, it requires a large quantity of training samples and is costly in terms of computational time. In order to solve these difficulties, a deep learning target detection framework combining texture features with concrete crack data is proposed. Texture features and pre-processed concrete data are merged to increase the number of feature channels in order to reduce the demand of training samples for the model and improve training speed. With this framework, concrete crack detection can be realized even with a limited number of samples. To accomplish this aim, self-made steel fiber reinforced concrete crack data is used for comparison between our framework and those without texture feature mergence or pre-processed concrete data. The experimental results show that the number of parameters that need to be fitted in the model training and training time can be correspondingly reduced and the detection accuracy can also be improved.

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“…However, vibration and displacement monitoring of bridges has shown challenges in detecting this type of damage, since global vibration characteristics (e.g., resonant frequency, damping ratio) change slightly with local damage [5,6]. In addition, most conventional testing methods (e.g., ultrasonic testing [7,8], impact echo measurement [9], thermographic images [10], and electro-magnetic radar [11,12]) detect damage near-surface of concrete structures, but face difficulty in exposing internal cracks, voids, and delamination located far beneath surfaces.…”

Section: Introductionmentioning

confidence: 99%

A Portable Scanning Device for Local Vibration Testing of Concrete Structures

Murakawa

Naito

Fujisaku

et al. 2021

Lecture Notes in Civil Engineering

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In this study, the effectiveness of local vibration testing using a portable scanner and machine learning for non-destructive inspection of concrete structures was examined. Using a portable vibrator and laser vibrometer, local throughthickness vibration tests were conducted on specimens of concrete beams and a railway track containing voids. Comparisons between frequency response functions measured over intact and void regions within structural concrete showed wave damping around the void. Moreover, a large amount of frequency response functions was obtained with the laser vibrometer in the scanning testing. With the measured data, Support Vector Machine (Kernel method) was used for detecting voids within the beams and beneath the railway track slab. Through its analysis of frequency response functions, the measured data of the intact or void condition was classified with a percent accuracy of 70%. This result indicates promising usage of the proposed method: utilizing a portable vibrator, laser vibrometer, and machine learning for non-destructive inspection of concrete structures.

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Surface and Subsurface Remote Sensing of Concrete Structures Using Synthetic Aperture Radar Imaging

Cited by 20 publications

References 18 publications

Corrosion detection of steel-reinforced concrete specimens using synthetic aperture radar

Corrosion detection of steel-reinforced concrete specimens using synthetic aperture radar

Crack Texture Feature Identification of Fiber Reinforced Concrete Based on Deep Learning

A Portable Scanning Device for Local Vibration Testing of Concrete Structures

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