• 전기학회논문지
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• 제59권6호
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• pp.1070-1074
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• 2010

In this study we deal with a simple computer simulation algorithm to decide the PRT station capacity. PRT system is different from the conventional rail traffic system in such point that the station is off-line so as to guarantee a very short headway. This characteristic has correlation with the accurate prediction of the line capacity and with the scale of the off-line station. In this paper physical factors and vehicles per hour that are necessary to decide the off-line station scale and the off-line station capacity, respectively, are shown through a simple compter simulations.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2005년도 추계 학술발표회 제17권2호
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• pp.93-96
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• 2005

Since the columns with flexure-shear failure have lower ductility than those with flexural failure, shear capacity curve models shall be applied as well as flexural capacity curve in order to determine ultimate displacement for seismic design or performance evaluation. In this paper, a modified shear capacity curve model is proposed and compared with the other models such as the CALTRANS model, Aschheim et al.'s model, and Priestley et al.'s model. Four shear capacity curve models are applied to the 4 full scale circular bridge column test results and the accuracy of each model is discussed. It may not be fully adequate to drive a final decision from the application to the limited number of test results, however the proposed model provides the better prediction of failure mode and ultimate displacement than the other models for the selected column test results.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2006년도 춘계학술발표회 논문집(I)
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• pp.186-189
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• 2006

Since the columns with flexure-shear failure have lower ductility than those with flexural failure, shear capacity curve models shall be applied as well as flexural capacity curve in order to determine ultimate displacement for seismic design or performance evaluation. In this paper, a proposed modified shear capacity curve model is compared with the other models such as the CALTRANS model, Aschheim et al.'s model, and Priestley et al.'s model. Four shear capacity curve models are applied to the 4 full scale and 7 small scale circular bridge column test results and the accuracy of each model is discussed. It may not be fully adequate to drive a final decision from the application to the limited number of test results, however the proposed model provides the better prediction of failure mode and ultimate displacement than the other models for the selected column test results.

• 콘크리트학회논문집
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• 제12권5호
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• pp.101-110
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• 2000

A practical approach of calculating the ultimate strength of composite beams with unreinforced web opning is proposed through shear behavioral tests. In this method, the slab shear contribution at the opening is calculated as the smaller value of the pullout capacity of shear connector at the high moment end and the one way shear capacity of slab. A simple interaction equation is used to predict the ultimate strength under simultaneous bending moment and shear force. Strength prediction by the proposed method is compared with previous test results and the predictions by other analytical methods. The comparison shows that the proposed method predicts the ultimate capacity with resonable accuracy.

• Computers and Concrete
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• 제10권4호
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• pp.331-347
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• 2012

This paper describes the varying material model, the analysis method and the software development for reinforced concrete circular columns confined by spiral or hoop transverse steel reinforcement and subjected to eccentric loading. The widely used Mander model of concentric loading is adapted here to eccentric loading by developing an auto-adjustable stress-strain curve based on the eccentricity of the axial load or the size of the compression zone to generate more accurate interaction diagrams. The prediction of the ultimate unconfined capacity is straight forward. On the other hand, the prediction of the actual ultimate capacity of confined concrete columns requires specialized nonlinear analysis. This nonlinear procedure is programmed using C-Sharp to build efficient software that can be used for design, analysis, extreme event evaluation and forensic engineering. The software is equipped with an elegant graphics interface that assimilates input data, detail drawings, capacity diagrams and demand point mapping in a single sheet. Options for preliminary design, section and reinforcement selection are seamlessly integrated as well. Improvements to KDOT Bridge Design Manual using this software with reference to AASHTO LRFD are made.

• Journal of Electrical Engineering and Technology
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• 제11권4호
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• pp.974-986
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• 2016

Reversible image watermarking, a type of digital data hiding, is capable of recovering the original image and extracting the hidden message with precision. A number of reversible algorithms have been proposed to achieve a high embedding capacity and a low distortion. While numerous algorithms for the achievement of a favorable performance regarding a small embedding capacity exist, the main goal of this paper is the achievement of a more favorable performance regarding a larger embedding capacity and a lower distortion. This paper therefore proposes a reversible data hiding algorithm for which a novel piecewise 2D auto-regression (P2AR) predictor that is based on a rhombus-embedding scheme is used. In addition, a minimum description length (MDL) approach is applied to remove the outlier pixels from a training set so that the effect of a multiple linear regression can be maximized. The experiment results demonstrate that the performance of the proposed method is superior to those of previous methods.

• Steel and Composite Structures
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• 제38권4호
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• pp.431-445
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• 2021

This paper presents experiments and theoretical analysis on shear behavior of eight concrete-encased square concrete-filled steel tube (CECFST) specimens and three traditional reinforced concrete (RC) specimens. A total of 11 specimens with the test parameters including the shear span-to-depth ratio, steel tube size and studs arrangement were tested to explore the shear performance of CECFST specimens. The failure mode, shear capacity and displacement ductility were thoroughly evaluated. The test results indicated that all the test specimens failed in shear, and the CECFST specimens enhanced by the interior CFST core exhibited higher shear capacity and better ductility performance than that of the RC specimens. When the other parameters were the same, the larger steel tube size, the smaller shear span-to-depth ratio and the existence of studs could lead to the more satisfactory shear behavior. Then, based on the compatible truss-arch model, a set of formulas were developed to analytically predict the shear strength of the CECFST members by considering the compatibility of deformation between the truss part, arch part and the steel tube. Compared with the calculated results based on several current design specifications, the proposed formulas could get more accurate prediction.

• Earthquakes and Structures
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• 제22권4호
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• pp.355-372
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• 2022

The study is part of an experimental program on full-scale Un-Reinforced Masonry (URM) wall panels strengthened with Textile reinforced mortars (TRM). Eight brick walls (two with and five without central opening), were tested under the diagonal tension (shear) test method in order to investigate the strengthening system effectiveness on the in-plane behaviour of the walls. All the URM panels consist of the innovative components, named "Orthoblock K300 bricks" with vertical holes and a thin layer mortar. Both of them have great capacity and easy application and can be constructed much more rapidly than the traditional bricks and mortars, increasing productivity, as well as the compressive strength of the masonry walls. Several parameters pertaining to the in-plane shear behaviour of the retrofitted panels were investigated, including shear capacity, failure modes, the number of layers of the external TRM jacket, and the existence of the central opening of the wall. For both the control and retrofitted panels, the experimental shear capacity and failure mode were compared with the predictions of existing prediction models (ACI 2013, TA 2000, Triantafillou 1998, Triantafillou 2016, CNR 2018, CNR 2013, Eurocode 6, Eurocode 8, Thomoglou et al. 2020). The experimental work allowed an evaluation of the shear performance in the case of the bidirectional textile (TRM) system applied on the URM walls. The results have shown that some analytical models present a better accuracy in predicting the shear resistance of all the strengthened masonry walls with TRM systems which can be used in design guidelines for reliable predictions.

• Steel and Composite Structures
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• 제47권6호
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• pp.759-779
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• 2023

The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.

• Computers and Concrete
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• 제33권3호
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• pp.285-299
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• 2024

Recently, many engineering computations have realized their digital transformation to Machine Learning (ML)-based systems. Predicting the behavior of a structure, which is mainly computed with structural analysis software, is an essential step before construction for efficient structural analysis. Especially in the seismic-based design procedure of the structures, predicting the lateral load capacity of reinforced concrete (RC) columns is a vital factor. In this study, a novel ML-based model is proposed to predict the maximum lateral load capacity of RC columns under varying axial loads or cyclic loadings. The proposed model is generated with a Deep Neural Network (DNN) and compared with traditional ML techniques as well as a popular commercial structural analysis software. In the design and test phases of the proposed model, 319 columns with rectangular and square cross-sections are incorporated. In this study, 33 parameters are used to predict the maximum lateral load capacity of each RC column. While some traditional ML techniques perform better prediction than the compared commercial software, the proposed DNN model provides the best prediction results within the analysis. The experimental results reveal the fact that the performance of the proposed DNN model can definitely be used for other engineering purposes as well.