• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.41 no.1
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• pp.30-36
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• 2009

Cracking of scored or creased lines on boards is a serious problem in converting process of corrugated fiberboard. It is important to reduce the possibility of score crack in advance by controlling the related quality factors of linerboard. To find out the key properties affecting score crack, we carried out the correlation analysis between score crack and physical properties of linerboards. Score crack was evaluated by visual rating on surface crack after folding a linerboard using laboratory folding resistance tester. Thickness of linerboard was the most important factor to score crack. The critical limits of thickness and strain can be determined by correlation analysis for reducing the possibility of score crack.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.41 no.1
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• pp.44-51
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• 2009

Proper test methods and instruments for evaluating score or creasing crack have not been provided, although score crack trouble occurs frequently in manufacturing corrugated containers. Because existing creasability tester has the limitation of the available thickness of test piece and folding rate, it cannot be used for corrugated fiberboards with high thickness. In this study, we developed the laboratory test instrument and the method to determine the score or creasing crack of corrugated fiberboard. This instrument can evaluate folding resistance of corrugated board without restriction on the folding rate and thickness of specimen. Corrugated fiberboard had the different folding behavior from linerboard when it was creased. By using this test machine, score crack can be objectively determined by folding test piece to the certain folding angle with constant folding rate.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.42 no.5
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• pp.31-36
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• 2010

When corrugated board is folded at the severely low humidity condition, crack can occur along the scored (or creased) lines of linerboard. This phenomenon is called as score (or crease) crack. It is mainly resulted from the excessive concentration of stress on the outer layer of linerboard. To overcome score crack, many approaches including the installation of constant temperature and humidity system, displacement of low grade raw material by long and strong fibers, or application of water have been tried. We examined the effect of the weight fraction of top layer in two-ply sheet, freeness of top layer stock and wet pressing on strain and elastic modulus of sheet to prevent score crack. Lower freeness and higher press load increased the density and elastic modulus of sheet. Pressing load over the $50kgf/cm^2$, however, decreased the strain of sheet. The weight fraction of top layer had positive effect on strain as well as elastic modulus without increasing the density of sheet.

• Smart Structures and Systems
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• v.29 no.1
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• pp.221-235
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• 2022

Manual inspection of steel box girders on long span bridges is time-consuming and labor-intensive. The quality of inspection relies on the subjective judgements of the inspectors. This study proposes an automated approach to detect and segment cracks in high-resolution images. An end-to-end cascaded framework is proposed to first detect the existence of cracks using a deep convolutional neural network (CNN) and then segment the crack using a modified U-Net encoder-decoder architecture. A Naïve Bayes data fusion scheme is proposed to reduce the false positives and false negatives effectively. To generate the binary crack mask, first, the original images are divided into 448 × 448 overlapping image patches where these image patches are classified as cracks versus non-cracks using a deep CNN. Next, a modified U-Net is trained from scratch using only the crack patches for segmentation. A customized loss function that consists of binary cross entropy loss and the Dice loss is introduced to enhance the segmentation performance. Additionally, a Naïve Bayes fusion strategy is employed to integrate the crack score maps from different overlapping crack patches and to decide whether a pixel is crack or not. Comprehensive experiments have demonstrated that the proposed approach achieves an 81.71% mean intersection over union (mIoU) score across 5 different training/test splits, which is 7.29% higher than the baseline reference implemented with the original U-Net.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.35-42
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• 2020

In this study, we propose a crack detection method using limited data with a U-Net based image inpainting technique that is a modified unsupervised anomaly detection method. Concrete cracking occurs due to a variety of causes and is a factor that can cause serious damage to the structure in the long term. In general, crack investigation uses an inspector's visual inspection on the concrete surfaces, which is less objective in judgment and has a high possibility of human error. Therefore, a method with objective and accurate image analysis processing is required. In recent years, the methods using deep learning have been studied to detect cracks quickly and accurately. However, when the amount of crack data on the building or infrastructure to be inspected is small, existing crack detection models using it often show a limited performance. Therefore, in this study, an unsupervised anomaly detection method was used to augment the data on the object to be inspected, and as a result of learning using the data, we confirmed the performance of 98.78% of accuracy and 82.67% of harmonic average (F1_Score).

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.39 no.2 s.120
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• pp.38-44
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• 2007

The hygroscopic property of paper is important for convertability and end use performance. When the board and corrugated fiberboard are exposed to low relative humidity, a trouble of score (or crease) cracking could occur. In this study, we evaluated the moisture content and mechanical properties of linerboard and corrugated board at the cyclic condition of low humidity to prevent a score crack trouble. As the relative humidity decreased from 50% to 38% and 25%, the moisture content of linerboard decreased about 7% to 6% and 4%. At low humidity, most of mechanical properties were improved except for strain. The linerboard exposed at 25% RH showed a remarkable reduction of strain by 11%. At the same relative humidity, linerboard and corrugated fiberboard showed the different property values depending on moisture hysteresis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3797-3822
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• 2020

The inspection of cracks on the surface of tunnel linings is a common method of evaluate the condition of the tunnel. In particular, determining the thickness and shape of a crack is important because it indicates the external forces applied to the tunnel and the current condition of the concrete structure. Recently, several automatic crack detection methods have been proposed to identify cracks using captured tunnel lining images. These methods apply an image-segmentation mechanism with well-annotated datasets. However, generating the ground truths requires many resources, and the small proportion of cracks in the images cause a class-imbalance problem. A weakly annotated dataset is generated to reduce resource consumption and avoid the class-imbalance problem. However, the use of the dataset results in a large number of false positives and requires post-processing for accurate crack detection. To overcome these issues, we propose a crack detection method using a ternary classifier. The proposed method significantly reduces the false positive rate, and the performance (as measured by the F1 score) is improved by 0.33 compared to previous methods. These results demonstrate the effectiveness of the proposed method.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.371-393
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• 2023

Crack detection in structures plays a vital role in ensuring their safety, durability, and reliability. Traditional crack detection methods sometimes need significant manual inspections, which are laborious, expensive, and prone to error by humans. Deep learning algorithms, which can learn intricate features from large-scale datasets, have emerged as a viable option for automated crack detection recently. This study presents an in-depth review of crack detection methods used till now, like image processing, traditional machine learning, and deep learning methods. Specifically, it will provide a comparative analysis of crack detection methods using deep learning, aiming to provide insights into the advancements, challenges, and future directions in this field. To facilitate comparative analysis, this study surveys publicly available crack detection datasets and benchmarks commonly used in deep learning research. Evaluation metrics employed to check the performance of different models are discussed, with emphasis on accuracy, precision, recall, and F1-score. Moreover, this study provides an in-depth analysis of recent studies and highlights key findings, including state-of-the-art techniques, novel architectures, and innovative approaches to address the shortcomings of the existing methods. Finally, this study provides a summary of the key insights gained from the comparative analysis, highlighting the potential of deep learning in revolutionizing methodologies for crack detection. The findings of this research will serve as a valuable resource for researchers in the field, aiding them in selecting appropriate methods for crack detection and inspiring further advancements in this domain.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.15-22
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• 2020

In this study, we propose an improvement method that can create U-Net model which detect fine concrete cracks by applying a weighted loss function. Because cracks in concrete are a factor that threatens safety, it is important to periodically check the condition and take prompt initial measures. However, currently, the visual inspection is mainly used in which the inspector directly inspects and evaluates with naked eyes. This has limitations not only in terms of accuracy, but also in terms of cost, time and safety. Accordingly, technologies using deep learning is being researched so that minute cracks generated in concrete structures can be detected quickly and accurately. As a result of attempting crack detection using U-Net in this study, it was confirmed that it could not detect minute cracks. Accordingly, as a result of verifying the performance of the model trained by applying the suggested weighted loss function, a highly reliable value (Accuracy) of 99% or higher and a harmonic average (F1_Score) of 89% to 92% was derived. The performance of the learning improvement plan was verified through the results of accurately and clearly detecting cracks.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.9
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• pp.35-44
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• 2014

This study was to secure the safety of poor appearance distribution concrete poles effectively and to reduce the replacement costs of them by developing a soundness evaluation index. The researcher of this study investigated poor appearance types of concrete pole, collected 53 of test samples, and tested pole strength. As a result of strength test, only 17 percent of poor appearance concrete poles were below 2.0 of safety factor spec. As results of multiple regression analysis, it is verified that surface air void, horizontal crack, net-shaped crack, elapsed year, vertical crack, and deterioration in concrete compressive strength have statistically negative effects on safety factor of concrete poles in a significant level. The researcher set up a soundness evaluation index by using multiple regression equation, and suggested that poor appearance concrete poles should be replaced or reinforced only in case of soundness evaluation score of 150 or above.