• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.91-96
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• 2005

In this study, researchers developed the estimative algorithm for artificial defects in semiconductor packages and performed it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Backpropagation Neural Network. Self-Organizing Map and Backpropagation Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages: Crack, Delamination and Normal. According to the results, we were confirmed that estimative algorithm was provided the recognition rates of $75.7\%$ (for Crack) and $83_4\%$ (for Delamination) and $87.2\%$ (for Normal).

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.6
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• pp.38-44
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• 2019

A Convolution Neural Network(CNN) model was utilized to detect surface cracks in asphalt concrete pavements. The CNN used for this study consists of five layers with 3×3 convolution filter and 2×2 pooling kernel. Pavement surface crack images collected by automated road surveying equipment was used for the training and testing of the CNN. The performance of the CNN was evaluated using the accuracy, precision, recall, missing rate, and over rate of the surface crack detection. The CNN trained with the largest amount of data shows more than 96.6% of the accuracy, precision, and recall as well as less than 3.4% of the missing rate and the over rate.

• 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.

• Geomechanics and Engineering
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• v.8 no.2
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• pp.237-255
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• 2015

From the related engineering principles, analytical solutions for horizontal crack initiation and propagation on a coal panel floor-underlying strata interface due to coal panel excavation are derived in this paper. Two important concepts, namely the critical panel width of horizontal crack initiation on the panel floor-underlying strata interface and the critical panel width of vertical fracture (crack) initiation in the panel floor, have been presented. The resulting analytical solution indicates that: (1) the first criterion can be used to express the condition under which horizontal plane cracks (on the panel floor-underlying strata interface or in the panel floor because of delamination) due to the mining induced vertical stress will initiate and propagate; (2) the second criterion can be used to express the condition under which vertical plane cracks (in the panel floor) due to the mining induced horizontal stress will initiate and propagate; (3) this orthogonal set of horizontal and vertical plane cracks, once formed, will provide the necessary weak network for the flow of gas to inrush into the panel. Two characteristic equations are given to quantitatively estimate both the critical panel width of vertical fracture initiation in the panel floor and the critical panel width of horizontal crack initiation on the interface between the panel floor and its underlying strata. The significance of this study is to provide not only some theoretical bases for understanding the fundamental mechanism of a longwall floor gas inrush problem but also a benchmark solution for verifying any numerical methods that are used to deal with this kind of gas inrush problem.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.05a
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• pp.677-682
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• 2001

In this study, a method to evaluate diffusion coefficient of chloride ion in cracked concrete is proposed. For cracked concrete having either anisotropic or isotropic crack network, each crack of saturated concrete is considered as a V shape crack, and an effective diffusion coefficient is expressed with diffusion coefficients of cracked part and noncracked part and a so-called crack spacing factor. A comparison with experimental results shows that the diffusion coefficient for cracked concrete is accurately predicted by the effective diffusion coefficient. Prediction results also show that the cracks in concrete markedly change the diffusion properties and accelerate penetration of drifting species. The method in this paper can be effectively used to consider the effect of cracks on concrete diffusion coefficient of cracked concrete.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.208-213
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• 2004

Methods for estimation of the effective stress intensity factor range (${\Delta}K_{eff}$) are evaluated for narrow and wide band random loading crack growth test data of 2024-T351 aluminum alloy. Three methods of determining $K_{op}$, visual measurement, ASTM offset compliance method, and the neural network method proposed by Kang and Song, and three methods of estimating ${\Delta}K_{eff}$, conventional, the 2/PI0 and 2/PI methods proposed by Donald and Paris, are compared in a quantitative manner by using the results of fatigue crack growth life prediction under random loading. For all $K_{op}$ determination methods discussed, the 2/PI0 and 2/PI methods of estimating ${\Delta}K_{eff}$ provide better results than conventional method for narrow and wide band random loading data.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.05a
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• pp.716-719
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• 2004

This study proposes an algorithm for recognition of crack patterns, which includes horizontal, vertical, diagonal$(-45^{\circ})$, diagonal$(+45^{\circ})$, and random cracks, based on image processing technique and artificial neural network. A MATLAB code was developed for the proposed algorithm, and then numerical tests were performed on thirty-eight crack images to examine validity of the algorithm. Within the limited tests in the present study, the proposed algorithm was revealed as accurately recognizing the crack patterns when compared to those classified by a human expert.

• Smart Structures and Systems
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• v.25 no.4
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• pp.459-469
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• 2020

The presence of cracks in mechanical components is a very important problem that, if it is not detected on time, can lead to high economic costs and serious personal injuries. This work presents a methodology focused on identifying cracks in unbalanced rotors, which are some of the most frequent mechanical elements in industry. The proposed method is based on Artificial Neural Networks that give a solution to the presented inverse problem. They allow to estimate unknown crack parameters, specifically, the crack depth and the eccentricity angle, depending on the dynamic behavior of the rotor. The necessary data to train the developed Artificial Neural Network have been obtained from the frequency spectrum of the displacements of the well- known cracked Jeffcott rotor model, which takes into account the crack breathing mechanism during a shaft rotation. The proposed method is applicable to any rotating machine and it could contribute to establish adequate maintenance plans.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.377-380
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• 2023

본 논문에서는 합성곱 신경망(Convolutional Neural Networks, CNN)과 탄성왜곡(Elastic Distortion) 기법을 통한 데이터 증강 기법을 활용하여 학습 데이터를 구축하는 프레임워크를 제안한다. 실제 균열 이미지는 정형화된 형태가 없고 복잡한 패턴을 지니고 있어 구하기 어려울 뿐만 아니라, 데이터를 확보할 때 위험한 상황에 노출될 우려가 있다. 이러한 데이터베이스 구축 문제점을 본 논문에서 제안하는 데이터 증강 기법을 통해 비용적, 시간적 측면에서 효율적으로 해결한다. 세부적으로는 DeepCrack의 데이터를 10배 이상 증가하여 실제 균열의 특징을 반영한 메타 데이터를 생성하여 U-net을 학습하였다. 성능을 검증하기 위해 균열 탐지 연구를 진행한 결과, IoU 정확도가 향상되었음을 확인하였다. 데이터를 증강하지 않았을 경우 잘못 예측(FP)된 경우의 비율이 약 25%였으나, 데이터 증강을 통해 3%까지 감소하였음을 확인하였다.

• 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.