• Steel and Composite Structures
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• 제23권4호
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• pp.421-433
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• 2017

Experimental and numerical studies of a newly developed Reduced Beam Section (RBS) connection, called Tubular Web RBS connection (TW-RBS) have been recently conducted. This paper presents experimental and numerical results of extending the plastic hinge length on the beam flange to increase energy dissipation of a proposed version of the TW-RBS connection with two pipes, (TW-RBS(II)), made by replacing a part of flat web with two steel tubular web at the desirable location of the beam plastic hinge. Two deep-beam specimens with two pipes are prepared and tested under cyclic loads. Obtained results reveal that the TW-RBS(II) like its type I, increases story drift capacity up to 6% in deep beam much more than that stipulated by the current seismic codes. Based on test results, the proposed TW-RBS(II) helps to dissipate imposed energy up to 30% more than that of the TW-RBS(I) specimens at the same story drift and also reduces demands at the beam-to-column connection up to 30% by increasing plastic hinge length on the beam flange. The TW-RBS(II) specimens are finally simulated using finite element method showing good agreement with experimental results.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2001년도 추계학술발표대회 개요집
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• pp.121-124
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• 2001

The main characteristic of the Electron Beam Welding technique is its high energy density which produces thin and deep welds with very little distortion. High accelerated electrons, focused in a beam of 0.5 ∼ 2mm diameter, produce narrow welds with deep penetration. The result is a small HAZ as well as a low and uniform distortion which is predictible within very narrow limits. But the small diameter of the EB increases the requirements for the equipment control system for centering the beam on the welding joint in order to avoid any lack of fusion. Therefore, in this paper, we introduce the system developed at our company and the quality of welding zone, the detail function of system.

• Structural Engineering and Mechanics
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• 제77권1호
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• pp.47-56
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• 2021

This paper deals with damage detection using a Gapped Smoothing Method (GSM) combined with deep learning. Convolutional Neural Network (CNN) is a model of deep learning. CNN has an input layer, an output layer, and a number of hidden layers that consist of convolutional layers. The input layer is a tensor with shape (number of images) × (image width) × (image height) × (image depth). An activation function is applied each time to this tensor passing through a hidden layer and the last layer is the fully connected layer. After the fully connected layer, the output layer, which is the final layer, is predicted by CNN. In this paper, a complete machine learning system is introduced. The training data was taken from a Finite Element (FE) model. The input images are the contour plots of curvature gapped smooth damage index. A free-free beam is used as a case study. In the first step, the FE model of the beam was used to generate data. The collected data were then divided into two parts, i.e. 70% for training and 30% for validation. In the second step, the proposed CNN was trained using training data and then validated using available data. Furthermore, a vibration experiment on steel damaged beam in free-free support condition was carried out in the laboratory to test the method. A total number of 15 accelerometers were set up to measure the mode shapes and calculate the curvature gapped smooth of the damaged beam. Two scenarios were introduced with different severities of the damage. The results showed that the trained CNN was successful in detecting the location as well as the severity of the damage in the experimental damaged beam.

• Computers and Concrete
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• 제10권6호
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• pp.609-630
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• 2012

Currently, the design of reinforced concrete deep beams with web openings is carried out using empirical or semi-empirical methods and hence their scope of application is limited. In particular, high strength concrete deep beams with various web opening configurations have been given little treatment. In view of this, a nonlinear layered finite element method (LFEM) for cracking and failure analysis of reinforced concrete structures is used to conduct a parametric study to investigate reinforced concrete deep beams various web opening behaviours. This paper initially presents comparisons of LFEM output with published test results to numerical techniques. The paper then focuses on a parametric study on the shear strengths of deep beams with varying web opening configurations such as opening sizes and locations. The results confirm that the current design methods are inadequate in predicting the maximum shear strength when web openings are present. A series of parametric study offers insight into the maximum shear strength of the deep beams being critically influenced by the size and location of web openings.

• Imaging Science in Dentistry
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• 제50권4호
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• pp.331-337
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• 2020

Purpose: As cone-beam computed tomography (CBCT) has become the most widely used 3-dimensional (3D) imaging modality in the dental field, storage space and costs for large-capacity data have become an important issue. Therefore, if 3D data can be stored at a clinically acceptable compression rate, the burden in terms of storage space and cost can be reduced and data can be managed more efficiently. In this study, a deep learning network for super-resolution was tested to restore compressed virtual CBCT images. Materials and Methods: Virtual CBCT image data were created with a publicly available online dataset (CQ500) of multidetector computed tomography images using CBCT reconstruction software (TIGRE). A very deep super-resolution (VDSR) network was trained to restore high-resolution virtual CBCT images from the low-resolution virtual CBCT images. Results: The images reconstructed by VDSR showed better image quality than bicubic interpolation in restored images at various scale ratios. The highest scale ratio with clinically acceptable reconstruction accuracy using VDSR was 2.1. Conclusion: VDSR showed promising restoration accuracy in this study. In the future, it will be necessary to experiment with new deep learning algorithms and large-scale data for clinical application of this technology.

• Journal of Welding and Joining
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• 제15권3호
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• pp.99-108
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• 1997

For the conventional welding method, the high heat transfer makes the crystallization of the work material unavoidable. Whereas the laser is able to weld the amorphous metal without a crystallized zone, because heat transfer is limited withn a very small restricted volume. In this paper, the possibilities and the limits of the laser welding in a deep frozen environment by liquid nitrogen were studied to utilize the advantageous properties of amorphous metal foils. The author investigated, after laser welding in a deep frozen environment with a solid state laser (Nd:YAG-laser), the achievable strengths and the influences of the laser beam parameters on the strengths.

• 천문학회보
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• 제45권1호
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• pp.66.2-66.2
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• 2020

We propose a deep learning analysis technique with a convolutional neural network (CNN) to predict the evolutionary track of the Epoch of Reionization (EoR) from the 21-cm differential brightness temperature tomography images. We use 21cmFAST, a fast semi-numerical cosmological 21-cm signal simulator, to produce mock 21-cm maps between z = 6 ~ 13. We then apply two observational effects, such as instrumental noise and limit of (spatial and depth) resolution somewhat suitable for realistic choices of the Square Kilometre Array (SKA), into the 21-cm maps. We design our deep learning model with CNN to predict the sliced-averaged neutral hydrogen fraction from the given 21-cm map. The estimated neutral fraction from our CNN model has great agreement with the true value even after coarsely smoothing with broad beam size and frequency bandwidth and heavily covered by noise with narrow beam size and frequency bandwidth. Our results show that the deep learning analyzing method has the potential to reconstruct the EoR history efficiently from the 21-cm tomography surveys in future.

• Advances in aircraft and spacecraft science
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• 제1권3호
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• pp.253-271
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• 2014

A linearised buckling analysis of thin-walled beams is addressed in this paper. Beam theories formulated according to a unified approach are presented. The displacement unknown variables on the cross-section of the beam are approximated via Mac Laurin's polynomials. The governing differential equations and the boundary conditions are derived in terms of a fundamental nucleo that does not depend upon the expansion order. Classical beam theories such as Euler-Bernoulli's and Timoshenko's can be retrieved as particular cases. Slender and deep beams are investigated. Flexural, torsional and mixed buckling modes are considered. Results are assessed toward three-dimensional finite element solutions. The numerical investigations show that classical and lower-order theories are accurate for flexural buckling modes of slender beams only. When deep beams or torsional buckling modes are considered, higher-order theories are required.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2004년도 춘계 학술발표회 제16권1호
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• pp.708-711
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• 2004

A typical structure was selected for a prototype and four 1:2.5 scaled models, representing the subassemblage including the interior column and the deep beam, were constructed. The transverse reinforcement was designed according to ACI procedure and the procedure proposed by Sheikh. In this study, the correlation between the experimental and analytical responses of the subassemblages subjected to the cyclic lateral displacement were evaluated through investigation of lateral load-lateral deformation, local deformation characteristics by using a nonlinear FEM analysis program RCAHEST.

• 한국강구조학회 논문집
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• 제16권5호통권72호
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• pp.575-586
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• 2004

본 논문은 새롭게 개발된 춤이 깊은 데크 플레이트(이하 Deep Deck) 및 비대칭 H형강 하부플랜지에 플레이트를 용접한 비대칭 철골보로 구성된 매립형(슬림플로어) 합성보의 웨브면에 개구부를 가진 유공 합성보의 휨성능에 관한 실험적 연구이다. 실험은 철골보 춤(250mm, 300mm), 개구부의 위치 및 가력조건에 따라 5개의 유공 합성보에 대한 단순지지 휨실험 수행하였다. 실험결과, 매립형 유공합성보는 전단연결재를 사용하지 않고도 콘크리트의 면내 부착력에 의해 충분한 합성작용을 하는 것으로 나타났고, 개구부 주변이 콘크리트에 의해 구속되어 개구부의 보강없이 충분한 구조적 성능을 발휘할 수 있음을 확인하였다.