• Computers and Concrete
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• 제2권6호
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• pp.481-498
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• 2005

A wavelet based approach is proposed for structural damage detection in beams, plate and delamination of composite plates. Wavelet theory is applied here for crack identification of a beam element with a transverse on edge non-propagating open crack. Finite difference method was used for generating a general displacement equation for the cracked beam in the first example. In the second and third example, damage is detected from the deformed shape of a loaded simply supported plate applying the wavelet theory. Delamination in composite plate is identified using wavelet theory in the fourth example. The main concept used is the breaking down of the dynamic signal of a structural response into a series of local basis function called wavelets, so as to detect the special characteristics of the structure by scaling and transformation property of wavelets. In the light of the results obtained, limitations of the proposed method as well as suggestions for future work are presented. Results show great promise of wavelet approach for damage detection and structural health monitoring.

• Structural Monitoring and Maintenance
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• 제9권3호
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• pp.221-235
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• 2022

Among nondestructive damage detection methods, impedance-based methods have been recognized as an effective technique for damage identification in many kinds of structures. This paper proposes a method to detect cracks in metal structures by combining electro-mechanical impedance (EMI) responses and artificial neural networks (ANN). Firstly, the theories of EMI responses and impedance-based damage detection methods are described. Secondly, the reliability of numerical simulations for impedance responses is demonstrated by comparing to pre-published results for an aluminum beam. Thirdly, the proposed method is used to detect cracks in the beam. The RMSD (root mean square deviation) index is used to alarm the occurrence of the cracks, and the multi-layer perceptron (MLP) ANN is employed to identify the location and size of the cracks. The selection of the effective frequency range is also investigated. The analysis results reveal that the proposed method accurately detects the cracks' occurrence, location, and size in metal structures.

• 비파괴검사학회지
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• 제30권6호
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• pp.564-568
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• 2010

When ultrasound propagates to a crack, transmitted and reflected waves are generated. These waves have useful information for the detection of the crack lying in a structure. When a crack is under residual stress, crack surfaces will contact each other and a closed crack is formed. For closed cracks, the fundamental component of the reflected and transmitted waves will be weak, and as such it is not easy to detect them. In this case, higher harmonic components will be useful. In this paper, nonlinear characteristic of a closed crack is modeled by a continuum material having a tensile-compressive unsymmetry, and the amplitude of the second harmonic wave was obtained by spectrum analysis. Variation of the second harmonic component depending on the nonlinearity of the inclusion was investigated. Two-dimensional plane strain model is considered, and finite element software ABAQUS/Explicit is used.

• Smart Structures and Systems
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• 제10권6호
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• pp.547-555
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• 2012

Measuring crack length in concrete fracture test is not a trivial problem due to high speed crack propagation. In this study, mechanoluminascent (ML) material, which emits visible light under stress condition, was employed to visualize crack propagation during concrete fracture test. Three-point bending test was conducted with a notched concrete beam specimen. The cracking images due to ML phenomenon were recorded by using a high speed camera as a function of time and external loadings. The experimental results successfully demonstrated the capability of ML material as a promising visualization tool for concrete crack propagation. In addition, an interesting cracking behavior of concrete bending fracture was observed in which the crack propagated fast while the load decreased slowly at early fracture stage.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.1577-1578
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• 2013

• Current Research on Agriculture and Life Sciences
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• 제32권4호
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• pp.231-235
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• 2014

이 연구는 파각란 선별기 도입을 비용 편익분석 목적으로 수행되었다. 계란 파각란 선별기를 도입할 때의 총수입과 총비용 평가액의 추정결과는 다음과 같다. 총비용의 경우 구입가격, 고정비용 및 유동비용의 합계로 나타내었고, 할인율에 따라 5%, 10%일 때 각각 232,904천원과 242,904천원으로 산정되었다. 계란 파각란 선별기 평가액은 할인율에 따라 228,543천원(할인율 5%인 경우)과 218,543천원(할인율 10%인 경우)으로 추정되었다(Table 6). 파각란 선별기 기술가치에 대한 평가는 B/C비율, 순현재가치(NPV), 내부수익율(IRR)의 값으로 판단되었고, 내부수익율 IRR의 값은 가정했던 할인율보다 훨씬 높게 나타났고, 순현재가치도 0보다 큰 값으로 나타났으며, B/C비율도 1.0 이상으로 나타나 경제적 타당성을 가지는 것으로 판단되었다(Table 7).

• 대한조선학회논문집
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• 제59권4호
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• pp.192-199
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• 2022

With the advent of autonomous ships, it is emerging as one of the very important issues not only to operate with a minimum crew or unmanned ships, but also to secure the safety of ships to prevent marine accidents. On-site inspection of the hull is mainly performed by the inspector's visual inspection, and video information is recorded using a small camera if necessary. However, due to the shortage of inspection personnel, time and space constraints, and the pandemic situation, the necessity of introducing an automated inspection system using artificial intelligence and remote inspection is becoming more important. Furthermore, research on hardware and software that enables the automated inspection system to operate normally even under the harsh environmental conditions of a ship is absolutely necessary. For automated inspection systems, it is important to review artificial intelligence technologies and equipment that can perform a variety of hull failure detection and classification. To address this, it is important to classify the hull failure. Based on various guidelines and expert opinions, we divided them into 6 types(Crack, Corrosion, Pitting, Deformation, Indent, Others). It was decided to apply object detection technology to cracks of hull failure. After that, YOLOv5 was decided as an artificial intelligence model suitable for survey and a common hull crack dataset was trained. Based on the performance results, it aims to present the possibility of applying artificial intelligence in the field by determining and testing the equipment required for survey.

• Smart Structures and Systems
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• 제29권1호
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• pp.29-39
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• 2022

Crack detection plays an important role in the maintenance and protection of steel box girder of bridges. However, since the cracks only occupy an extremely small region of the high-resolution images captured from actual conditions, the existing methods cannot deal with this kind of image effectively. To solve this problem, this paper proposed a novel three-stage method based on deep learning technology and morphology operations. The training set and test set used in this paper are composed of 360 images (4928 × 3264 pixels) in steel girder box. The first stage of the proposed model converted high-resolution images into sub-images by using patch-based method and located the region of cracks by CBAM ResNet-50 model. The Recall reaches 0.95 on the test set. The second stage of our method uses the Attention U-Net model to get the accurate geometric edges of cracks based on results in the first stage. The IoU of the segmentation model implemented in this stage attains 0.48. In the third stage of the model, we remove the wrong-predicted isolated points in the predicted results through dilate operation and outlier elimination algorithm. The IoU of test set ascends to 0.70 after this stage. Ablation experiments are conducted to optimize the parameters and further promote the accuracy of the proposed method. The result shows that: (1) the best patch size of sub-images is 1024 × 1024. (2) the CBAM ResNet-50 and the Attention U-Net achieved the best results in the first and the second stage, respectively. (3) Pre-training the model of the first two stages can improve the IoU by 2.9%. In general, our method is of great significance for crack detection.

• 한국터널지하공간학회 논문집
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• 제24권6호
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• pp.583-598
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• 2022

인력기반 터널 점검은 점검자의 주관적인 판단에 영향을 받으며 지속적인 이력관리가 어렵다. 따라서 최근에는 딥러닝 기반 자동 균열 탐지 연구가 활발히 진행되고 있다. 하지만 대부분의 연구에서는 사용하는 대규모 공개 균열 데이터셋은 터널 내부에서 발생하는 균열과 매우 상이하다. 또한 현행 터널 상태평가에서 정교한 균열 레이블을 구축하기 위해서는 추가적인 작업이 요구된다. 이에 본 연구는 균열 형상이 다소 단순하게 표현된 기존 데이터셋을 딥러닝 모델에 입력하여 균열 탐지 성능을 개선하는 방안을 제시한다. 기존 터널 데이터셋, 고품질 터널 데이터셋과 공개 균열 데이터셋을 조합하여 학습한 딥러닝 모델의 성능 평가와 비교를 수행한다. 그 결과 Cross Entropy 손실함수를 사용한 DeepLabv3+에 공개 데이터셋, 패치 단위 분류와 오버샘플링을 수행한 터널 데이터셋을 모두 학습한 경우 성능이 가장 좋았다. 향후 기 구축된 터널 영상 취득 시스템 데이터를 딥러닝 모델 학습에 효율적으로 활용하기 위한 방안을 수립하는 데 기여할 것으로 기대한다.

• 비파괴검사학회지
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• 제32권4호
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• pp.369-375
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• 2012

기존 비파괴평가 기술의 결함 탐지 한계를 극복하기 위하여 비선형 초음파공명 특성을 이용한 미세 균열 진단 기술을 개발하였다. 가진 전압에 따른 초음파공명 주파수의 천이현상과 정규화 공명 진폭 감소 현상을 비선형 초음파공명 특성 파라미터로 제안하였으며 이를 실험적으로 확인하였다. CT 시편에 피로시험을 통하여 미세한 자연 균열을 생성하였으며 피로 사이클 단계마다 초음파공명주파수와 정규화 공명진폭의 변화를 측정하였다. 무결함 또는 10 ${\mu}m$ 정도의 매우 미세한 균열이 존재하는 시편에서는 초음파공명 주파수 천이현상이나 정규화 공명 진폭의 변화가 나타나지 않는 반면에 30 ${\mu}m$급 이상의 미세 균열 시편에서는 균열 크기가 증가함에 따라 초음파공명주파수의 천이 현상이나 정규화 공명 진폭의 감소량이 증가함을 확인하였다.