• 한국구조물진단유지관리공학회 논문집
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• 제3권4호
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• pp.215-220
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• 1999

Concrete micro-cracks that are grown while the structures are under construction or in service, propagate gradually or rapidly by external forces and environmental effects. As described above, almost concrete structures generally have cracks, so for the safety and durability of structures, studies to detect cracks using nondestructive tests have been treated in great deal. The purpose of this study is to evaluate characteristics of AE signals detected from notched concrete beams bending test with different loading using one of nondestructive test, Acoustic Emission (AE) method. Furthermore this study predicts the location of initial crack and measures direction of crack propagation for on-line monitoring before the crack really grows in structures by using two-dimensional AE source location based on rectangular method with three-point bending test. This will allow efficient maintenance of concrete structures through monitoring of internal cracking based on acoustic emission method.

• 국제초고층학회논문집
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• 제9권4호
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• pp.351-360
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• 2020

Monitoring civil structures periodically is necessary for ensuring the fitness of the structures. Cracks on inner and outer surfaces of the building plays a vital role in indicating the health of the building. Conventionally, human visual inspection techniques were carried up to human reachable altitudes. Monitoring of high rise infrastructures cannot be done using this primitive method. Also, there is a necessity for more accurate prediction of cracks on building surfaces for ensuring the health and safety of the building. The proposed research focused on developing an efficient crack classification model using Transfer Learning enabled EfficientNet (TL-EN) architecture. Though many other pre-trained models were available for crack classification, they rely on more number of training parameters for better accuracy. The TL-EN model attained an accuracy of 0.99 with less number of parameters on large dataset. A bench marked METU dataset with 40000 images were used to test and validate the proposed model. The surfaces of high rise buildings were investigated using vision enabled Unmanned Arial Vehicles (UAV). These UAV is fabricated with TL-EN model schema for capturing and analyzing the real time streaming video of building surfaces.

• Computers and Concrete
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• 제32권5호
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• pp.475-486
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• 2023

Nondestructive evaluation (NDE) is an important task of civil engineering structure monitoring and inspection, but minor damage such as small cracks in local structure is difficult to observe. If cracks continued expansion may cause partial or even overall damage to the structure. Therefore, monitoring and detecting the structure in the early stage of crack propagation is important. The crack detection technology based on machine vision has been widely studied, but there are still some problems such as bad recognition effect for small cracks. In this paper, we proposed a deep learning method based on sweep signals to evaluate concrete surface crack with a width less than 1 mm. Two convolutional neural networks (CNNs) are used to analyze the one-dimensional (1D) frequency sweep signal and the two-dimensional (2D) time-frequency image, respectively, and the probability value of average damage (ADPV) is proposed to evaluate the minor damage of structural. Finally, we use the standard deviation of energy ratio change (ERVSD) and infrared thermography (IRT) to compare with ADPV to verify the effectiveness of the method proposed in this paper. The experiment results show that the method proposed in this paper can effectively predict whether the concrete surface is damaged and the severity of damage.

• 동력기계공학회지
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• 제16권1호
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• pp.72-77
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• 2012

Acoustic emission (AE) is known to be sensitive to fracture process and so it was expected that AE data may propose as a means of monitoring the fracture information. The aim of this study is to analyze the characteristic of AE signal emitted from glass during Vickers indentation. To observe AE characteristics by surface effect, both glass and coating glass were studied. During Vickers indentation loading, AE signal resulted from penny-like crack is detected. During Vickers indentation unloading, AE signal resulted from both radial/median crack and lateral crack is detected. In case of indentation on glass, the emission energy(${\epsilon}$) is found to be approximately proportional to the fourth power of the crack length. In case of indentation on coating glass, the emission energy(${\epsilon}$) is approximately proportional to the crack length.

• 한국안전학회지
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• 제13권4호
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• pp.96-101
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• 1998

J-R curve tests were performed on 1T compact specimens of SA516 Gr. 70 carbon steels under reverse cyclic loading. A Direct-Current Potential Drop (DCPD) method, one of the nondestructive techniques to detect flaw of structure, is being increasingly used for monitoring crack initiation and stable crack growth in typical fracture mechanics specimens for J-R testing. In many aspects this method is simpler than the unloading compliance method. The objective of this paper is to evaluate the J-R Curve according to the crack length measurement techniques under reverse cyclic loading. In order to prove the reliability and repeatability of the DCPD method, the crack length measured by using DCPD method was compared to one determined from unloading compliance. Consequently, this DCPD method correlated well with J-R curves and crack extension measurements determined from unloading compliance method.

• 대한기계학회논문집A
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• 제36권7호
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• pp.713-721
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• 2012

피로균열성장을 유한요소 시뮬레이션하였다. 인장시험으로 얻는 기계적 성질만을 사용하여 피로균열성장거동을 예측하려고 하였다. 유한요소해석 결과 균열선단 부근 절점의 변위의 변화를 살펴 임계균열개구변위를 결정하였다. 균열선단 절점을 분리하여 균열성장을 시뮬레이션하였다. Paris 법칙의 지수를 결정하여 이미 발표된 값과 비교하였다. 균열닫힘을 고려한 유효 응력확대계수에 관하여 그렸을 때 더 일관성이 있는 결과를 얻었다.

• Smart Structures and Systems
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• 제30권1호
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• pp.17-34
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• 2022

For steel structures, fatigue cracks are critical damage induced by long-term cycle loading and distortion effects. Vision-based crack detection can be a solution to ensure structural integrity and performance by continuous monitoring and non-destructive assessment. A critical issue is to distinguish cracks from other features in captured images which possibly consist of complex backgrounds such as handwritings and marks, which were made to record crack patterns and lengths during periodic visual inspections. This study presents a parametric study on image-based crack identification for orthotropic steel bridge decks using captured images with complicated backgrounds. Firstly, a framework for vision-based crack segmentation using the atrous convolution-based Deeplapv3+ network (ACDN) is designed. Secondly, features on crack images are labeled to build three databanks by consideration of objects in the backgrounds. Thirdly, evaluation metrics computed from the trained ACDN models are utilized to evaluate the effects of obstacles on crack detection results. Finally, various training parameters, including image sizes, hyper-parameters, and the number of training images, are optimized for the ACDN model of crack detection. The result demonstrated that fatigue cracks could be identified by the trained ACDN models, and the accuracy of the crack-detection result was improved by optimizing the training parameters. It enables the applicability of the vision-based technique for early detecting tiny fatigue cracks in steel structures.

• 한국도로학회:학술대회논문집
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• 한국도로학회 2008년도 추계학술대회 논문집
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• pp.93-102
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• 2008

An energy based crack growth model is developed in this study to simulate the propagation of top-down cracking in asphalt pavements. A viscoelastic fracture mechanics approach, generalized J integral, is employed to model the crack growth of asphalt concrete. Laboratory fatigue crack propagation tests for three different asphalt mixtures are performed at various load levels, frequencies and temperatures. Disk-shaped specimens with a proper loading fixture and crack growth monitoring system are selected for the tests. It is observed from the tests that the crack propagation model based on the generalized J integral is independent of load levels and frequencies, while the traditional Paris' law model based on stress intensity factor is dependent of loading frequencies. However, both models are unable to take care of the temperature dependence of the mixtures. The fatigue crack propagation model proposed in this study has a good agreement between experimental and predicted crack growth lives, which implies that the energy based J integral could be a better parameter to describe fatigue crack propagation of viscoelastic materials such as asphalt mixtures.

• Computers and Concrete
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• 제21권6호
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• pp.727-739
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• 2018

Crack is the most common typical feature of concrete deterioration, so routine monitoring and health assessment become essential for identifying failures and to set up an appropriate rehabilitation strategy in order to extend the service life of concrete structures. At present, image segmentation algorithms have been applied to crack analysis based on inspection images of concrete structures. The results of crack segmentation offering crack information, including length, width, and area is helpful to assist inspectors in surface inspection of concrete structures. This study proposed an algorithm of image segmentation enhancement, named morphological segmentation based on edge detection-II (MSED-II), to concrete crack segmentation. Several concrete pavement and building surfaces were imaged as the study materials. In addition, morphological operations followed by cross-curvature evaluation (CCE), an image segmentation technique of linear patterns, were also tested to evaluate their performance in concrete crack segmentation. The result indicates that MSED-II compared to CCE can lead to better quality of concrete crack segmentation. The least area, length, and width measurement errors of the concrete cracks are 5.68%, 0.23%, and 0.00%, respectively, that proves MSED-II effective for automatic measurement of concrete cracks.

• Smart Structures and Systems
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• 제29권1호
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• pp.141-151
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• 2022

In recent years, the industry and research communities have focused on developing autonomous crack inspection approaches, which mainly include image acquisition and crack detection. In these approaches, mobile devices such as cameras, drones or smartphones are utilized as sensing platforms to acquire structural images, and the deep learning (DL)-based methods are being developed as important crack detection approaches. However, the process of image acquisition and collection is time-consuming, which delays the inspection. Also, the present mobile devices such as smartphones can be not only a sensing platform but also a computing platform that can be embedded with deep neural networks (DNNs) to conduct on-site crack detection. Due to the limited computing resources of mobile devices, the size of the DNNs should be reduced to improve the computational efficiency. In this study, an architecture called pruned crack recognition network (PCR-Net) was developed for the detection of structural cracks. A dataset containing 11000 images was established based on the raw images from bridge inspections. A pruning method was introduced to reduce the size of the base architecture for the optimization of the model size. Comparative studies were conducted with image processing techniques (IPTs) and other DNNs for the evaluation of the performance of the proposed PCR-Net. Furthermore, a modularly designed framework that integrated the PCR-Net was developed to realize a DL-based crack detection application for smartphones. Finally, on-site crack detection experiments were carried out to validate the performance of the developed system of smartphone-based detection of structural cracks.