• 한국정밀공학회지
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• 제16권11호
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• pp.158-165
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• 1999

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses hybrid neuro-genetic technique. Feed-forward multilayer neural networks trained by back-propagation are used to learn the input)the location and dept of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this neural network and genetic algorithm, it is possible to formulate the inverse problem. Neural network training algorithm is the back propagation algorithm with the momentum method to attain stable convergence in the training process and with the adaptive learning rate method to speed up convergence. Finally, genetic algorithm is used to fine the minimum square error.

• Structural Monitoring and Maintenance
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• 제9권3호
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• pp.289-303
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• 2022

In this study, the impact of assigned pixel labels on the accuracy of crack image identification of steel structures is examined by using an atrous separable convolution neural network (ASCNN). Firstly, images containing fatigue cracks collected from steel structures are classified into four datasets by assigning different pixel labels based on image features. Secondly, the DeepLab v3+ algorithm is used to determine optimal parameters of the ASCNN model by maximizing the average mean-intersection-over-union (mIoU) metric of the datasets. Thirdly, the ASCNN model is trained for various image sizes and hyper-parameters, such as the learning rule, learning rate, and epoch. The optimal parameters of the ASCNN model are determined based on the average mIoU metric. Finally, the trained ASCNN model is evaluated by using 10% untrained images. The result shows that the ASCNN model can segment cracks and other objects in the captured images with an average mIoU of 0.716.

• Structural Engineering and Mechanics
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• 제50권4호
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• pp.441-457
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• 2014

This paper presents a finite element based numerical model to solve two dimensional bi-material problems. A bi-material beam consisting of two phase materials ceramic and metal is modelled by finite element method. The beam is subjected simultaneously to mechanical and thermal loadings. The main objective of this study is the analysis of crack deviation located in the brittle material near the interface. The effect of temperature gradient, the residual stresses and applied loads on crack initiation, propagation and deviation are examined and highlighted.

• Smart Structures and Systems
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• 제25권4호
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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.

• Smart Structures and Systems
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• 제2권1호
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• pp.47-60
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• 2006

Bi-spectrum is a tool in the signal processing for identification of non-linear dynamic behvaiour in systems, and well-known for stationary system where components are non-linearly interacting. Breathing of a crack during shaft rotation is also exhibits a non-linear behaviour. The crack is known to generate 2X (twice the machine RPM) and higher harmonics in addition to 1X component in the shaft response during its rotation. Misaligned shaft also shows similar such feature as a crack in a shaft. The bi-spectrum method has now been applied on a small rotating rig to observe its features. The bi-spectrum results are found to be encouraging to distinguish these faults based on few experiments conducted on a small rig. The results are presented here.

• Computers and Concrete
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• 제19권5호
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• pp.509-514
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• 2017

Concrete retaining walls are the most common types of geotechnical structures for controlling instable slopes resulting from lateral pressure. In analytical stability, calculation of the concrete retaining walls is regarded as a rigid mass when its safety is required. When cracks in these structures are created, the stability may be enforced and causes to defeat. Therefore, identification, creation and propagation of cracks are among the important steps in control of lacks and stabilization. Using the numerical methods for simulation of crack propagation in concrete retaining walls bodies are among the new aspects of geotechnical analysis. Among the considered analytical methods in geotechnical appraisal, the boundary element method (BEM) for simulation of crack propagation in concrete retaining walls is very convenient. Considered concrete retaining wall of this paper is Pars Power Plant structured in south side in Assalouyeh, SW of Iran. This wall's type is RW6 with 11 m height and 440 m length and endurance of refinery construction lateral forces. To evaluate displacement and stress distributions (${\sigma}_{1,max}/{\sigma}_{3,min}$), the surrounding, especially in tip and its opening crack BEM, is considered an appropriate method. By considering the result of this study, with accurate simulation of crack propagation, it is possible to determine the final status of progressive failure in concrete retaining walls and anticipate the suitable stabilization method.

• 소성∙가공
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• 제26권4호
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• pp.210-215
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• 2017

In this paper, utilizing the theory of ductile fracture a chevron crack in a 4-stage open cold extrusion process is predicted by the finite element methods and then compared with previous experiments. The normalized Cockcroft-Latham damage model is employed and the material is identified using a tensile test based material identification technique that gives fracture information as well as flow stress at large strain. A large difference between the predicted cracks and actual experiments is observed, specifically narrower width and greater maximum height of the crack. This reveals the limitation of this approach based on the conventional theory of ductile fracture. Based on the observations and the related criticisms, a new approach for predicting the chevron crack is proposed, suggesting that either the critical damage should not be a fixed material constant, or that the conventional fracture theory should be considered with the effects of embrittlement due to accumulated plastic deformation while the duration of crack generation and plastic deformation should be reduced.

• 한국지반공학회논문집
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• 제18권6호
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• pp.153-160
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• 2002

본 연구에서는 터널 내공변위 계측데이터를 이용한. 라이닝 구조물의 손상도 평가기법에 대하여 논의하였다. 이를 위하여 정적으로 획득되는 내공변위 데이터와 라이닝의 자중을 고려하여 라이닝내 발생가능한 손상의 위치 및 정도를 파악할 수 있는 일종의 역해석 기법을 도입하였다. 특히 라이닝 요소내 강성도를 일정수준 저감하는 모형 1과 분산형 균열모델을 응용한 모형 2 등 두 가지 방법을 고려하였고 각각의 장단점을 비교.분석하였다. 이상적인 터널라이닝 구조물을 가정하여 두 가지 모형에 대한 수치해석을 실시하였으며 계측데이터에 포함되는 노이즈의 영향을 함께 고려하였다. 이 결과, 모형 1의 경우, 노이즈의 영향은 상대적으로 미미하나 내공변위에 민감하게 작용하므로 현장적용시 주의가 요구되며 모형 2의 경우에는 노이즈에 민감하게 작용하는 반면 강성도의 저감량이 미소하여 실제 터널라이닝 구조물의 손상파악에 손쉽게 적용될 수 있음을 보였다.

• 비파괴검사학회지
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• 제22권6호
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• pp.627-636
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• 2002

Identification and sizing of surface breaking vertical cracks using angle beam ultrasonic testing in practical situation quite often become very difficult tasks due to the presence of non-relevant signals caused by geometric reflectors. The present work introduces effective and systematic approaches to take care of such a difficulty by use oi angle beam ultrasonic testing models that can predict the expected signals from various targets very accurately. Specifically, the model-based TIFD (Technique for Identification of Flaw signals using Deconvolution) is Proposed for the identification of the crack tip signals from the non-relevant geometric reflection signals. In addition, the model-based Size-Amplitude Curve is introduced for the reliable sizing of surface breaking vertical cracks.

• Smart Structures and Systems
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• 제29권1호
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• pp.237-250
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• 2022

Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach.