Object status identification of X-ray CT images of microcapsule-based self-healing mortar

Wang, Xianfeng; Chen, Zhenpeng; Ren, Jun; Chen, Shaocong; Xing, Feng

doi:10.1016/j.cemconcomp.2021.104294

Cited by 21 publications

(6 citation statements)

References 36 publications

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“…Therefore, an encoder-decoder network was adopted with a CNN to achieve rapid and precise detection of barely seen micro-fractures. The organic microcapsules in cement were classified in [105] into five categories, namely microcapsules, ruptured microcapsules, pores, adhesive objects, and others, using a CVbased classifier.…”

Section: G Othersmentioning

confidence: 99%

“…Studies applying simple CNN architectures for X-ray image classification include [55] using a simple CNN model for weld defect classification, [105] using another simple CNN model for binary classification of automotive components into defective and non-defective class, and [2] using Evenly Distributed CNN (ED-CNN) illustrated structure in Fig. 7-a for casting defects classification.…”

Section: ) Deep Classification and Backbone Architecturesmentioning

confidence: 99%

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Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learningbased object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

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Section: G Othersmentioning

confidence: 99%

Section: ) Deep Classification and Backbone Architecturesmentioning

confidence: 99%

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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“…In addition, the fluorescent microcapsules were difficult to observe in a cement slurry, so the paraffin oil was selected as the dispersing phase of the microcapsules, the stirring speed (n 2 ) of which was calculated as 10,800 rpm through Figure 3 To mimic the proportions of the water-adaptive microcapsules in the cement slurry, 4 g of dry microcapsules was added to 59 mL of paraffin oil and stirred at 10,800 rpm for 3 min, the breakage behavior of which was observed by confocal laser scanning microscopy (CLSM, Nikon A1R+) under 488 nm excitation. 49,50 After absorbing water for 1 and 10 min, microcapsules were also broken according to the above operation and characterized respectively.…”

Section: Breakage Test Of Water-adaptive Microcapsulesmentioning

confidence: 99%

“…To mimic the proportions of the water-adaptive microcapsules in the cement slurry, 4 g of dry microcapsules was added to 59 mL of paraffin oil and stirred at 10,800 rpm for 3 min, the breakage behavior of which was observed by confocal laser scanning microscopy (CLSM, Nikon A1R+) under 488 nm excitation. , After absorbing water for 1 and 10 min, microcapsules were also broken according to the above operation and characterized respectively.…”

Section: Materials and Experimentsmentioning

confidence: 99%

Water-Adaptive Microcapsules with a Brittle–Ductile–Brittle Transition Based on an O/W/O Emulsion for the Self-Healing of Cementitious Materials

Ying,

Hu,

Han

et al. 2023

ACS Appl. Mater. Interfaces

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Cementitious materials inevitably develop cracks, posing a serious threat to the long-term security of infrastructure, especially in the complex underground environment of cementing engineering. Microcapsules are facing the problem of encapsulated structure damage during the mixing and breaking difficultly during self-healing when applied in cementitious materials, resulting in the decline of self-healing efficiency. Herein, the calcium alginate water-adaptive microcapsules (CaAlg-NS/E-51) were prepared via an O/W/O emulsion, and the water adaptability of the shell was applied to achieve a rapid brittle–ductile transition by absorbing water. The water adaptability of the microcapsule is conducive to resisting shear stress during stirring due to the decreased elastic modulus and the increased ductility of the shell when it absorbs water. Meanwhile, the water-bearing shell loses water and becomes brittle during dry curing, making it prone to fracture when self-healing. In the self-healing measurement, the self-healing efficiency of cementitious specimens with microcapsules absorbing water for 10 min improved by 234.9 and 60.0% at 1 and 7 days, respectively, compared with those containing dry microcapsules, owing to the water adaptability of the shell.

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“…Due to the great success and achievement in the medical field, micro-CT exhibited a great potential on investigating and providing valuable information on microstructures of the porous materials (Kato et al 2013(Kato et al , 2015Wang et al 2022b;Cnudde et al 2009;Promentilla et al 2009;du Plessis et al 2016). Numerous research works have been published in the recent years using multiscale X-ray CT for analyzing cement-based paste, mortar and concrete.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in X-ray Computed Tomography for Alkali-Activated Materials: A Review

Kong

Kato

Kurumisawa

2023

ACT

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Alkali-activated materials (AAMs) have been extensively studied due to their high performance and low carbon emissions. However, the long-term durability of AAMs remains a major concern, which is closely related to their microstructures and pore networks. X-ray computed tomography (CT) has emerged as a powerful non-destructive technique for threedimensional microstructural analysis in the field of civil engineering. While CT has been widely used to investigate cement-based materials, its application to AAMs is still limited. In this review, we provide a comprehensive summary of the background information and applications of CT in AAM research, focusing on pore structure analysis, phase identification, degradation analysis, fiber reinforcement, temporal evolution and simulations. Furthermore, the current challenges and future perspectives are also discussed.

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Object status identification of X-ray CT images of microcapsule-based self-healing mortar

Cited by 21 publications

References 36 publications

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Water-Adaptive Microcapsules with a Brittle–Ductile–Brittle Transition Based on an O/W/O Emulsion for the Self-Healing of Cementitious Materials

Recent Advances in X-ray Computed Tomography for Alkali-Activated Materials: A Review

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