• 펄프종이기술
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• 제41권1호
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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.

• 펄프종이기술
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• 제41권1호
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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.

• 펄프종이기술
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• 제42권5호
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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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• 제29권1호
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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.

• 한국컴퓨터정보학회논문지
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• 제25권10호
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• pp.35-42
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• 2020

본 연구에서는 비지도 이상 탐지 방법을 변형한 U-Net 기반의 이미지 복원 기법을 통해 한정적인 데이터를 활용한 균열 탐지 방안을 제안한다. 콘크리트 균열은 다양한 원인으로 인해 발생하며, 장기적으로 구조물의 심각한 손상을 초래할 수 있는 요소이다. 일반적으로 균열 조사는 검사원의 육안으로 판단하는 외관 검사법을 사용하는데, 이는 판단에 객관성이 떨어지며 인적 오류 발생 가능성이 크다. 따라서 객관적이고 정확한 이미지 분석 처리를 통한 방법이 요구된다. 최근에는 균열을 신속하고 정밀하게 탐지할 수 있도록 딥러닝을 활용한 기술들이 연구되고 있다. 하지만 일반적인 균열자료에 비해 점검 대상물에 대한 데이터는 한정적이므로 이를 활용한 기존 균열 탐지 모델의 성능은 제한적인 경우가 많다. 따라서 본 연구에서는 비지도 이상 탐지 방법을 사용해 점검 대상물에 대한 데이터를 증강하여 해당 데이터를 사용하여 학습한 결과, 정확도 98.78%, 조화평균(F1_Score) 82.67%의 성능을 확인하였다.

• 펄프종이기술
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• 제39권2호
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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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• 제14권9호
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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.

• 대한원격탐사학회지
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• 제39권4호
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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.

• 한국컴퓨터정보학회논문지
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• 제25권10호
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• pp.15-22
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• 2020

본 연구에서는 가중치 오차 함수를 적용하여, 미세한 콘크리트 균열을 감지하는 U-Net 모델을 만들 수 있도록 개선 방안을 제안한다. 콘크리트 균열은 안전을 위협하는 요소이기 때문에 그 상태를 주기적으로 파악하고 신속하게 초기 대응을 하는 것이 중요하다. 하지만 현재는 점검자가 직접 육안으로 검사하고 평가하는 외관 검사법이 주로 사용되고 있다. 이는 정확성뿐만 아니라 비용과 시간, 안전성 측면에서도 한계점을 가진다. 이에 콘크리트 구조물에 생성되는 미세한 균열을 신속하고 정밀하게 탐지할 수 있도록 딥러닝을 활용한 기술들이 연구되고 있다. 본 연구에서 U-Net을 활용한 균열 탐지를 시도한 결과, 미세한 균열을 탐지하지 못하는 것을 확인하였다. 이에 제시한 가중치 오차 함수를 적용하여 학습한 모델에 대해 성능을 검증한 결과, 정확도(Accuracy) 99% 이상, 조화평균(F1_Score) 89%에서 92%의 신뢰성 높은 수치를 도출해내었고, 미세한 균열을 정확하고 선명하게 탐지한 결과를 통해 학습 개선 방안의 성능을 검증하였다.

• 조명전기설비학회논문지
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• 제28권9호
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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.