• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2001년도 가을 학술발표회 논문집
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• pp.885-890
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• 2001

For the shear strength prediction of reinforced concrete beams, this paper considered the bending moment effect. Experimental results of the thirty-seven reinforced concrete beams were compared with analytical results by the FA-STM, TATM and TATM considered bending moment effect. While Ratios of test results to analytical results by using the truss models does not considered the bending moment effect decreased as shear span ratio increased, those by using the proposed method considered that were almost constant regardless of the increase of the shear span ratio. Predicted results obtained from proposed method agreed well with the experimental results.

• 대한가정학회지
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• 제30권4호
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• pp.153-165
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• 1992

The objectives of this study were to develop a probabilistic model for both hypotheses testing and mobility prediction. Methodologies being used for the analysis include multivariated analysis for descriptive statistics and logit model for hypotheses testing and prediction. The study used questionaire survey data conducted by Korean Research Institute for Human Settlements (KRIHS) in 1988. There were a total of 1,620 Samples, and both SPSS and Limdep software packages were used for statistical analysis and model testing. The major findings were highlighted as follows; The residential mobility over family life span by the social class were developed with the use of the probability model. Most of households in low class moved downwardly. They had lived the small-owned single detached house in first family life span and moved into the small-rented single detached house in next family life span. Most of households in middle class moved upwardly. They had lived the small-owned apartment in first family life span and moved into the large-owned single detached house in last family life span. Most of households in high class horizontally. They had lived the large-owned single detached house in first family life span and moved into the same one except in last family life span.

• 한국산학기술학회논문지
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• 제7권6호
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• pp.1012-1020
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• 2006

철도차량의 차륜에서 발생하는 크랙은 표면으로부터 개시하는 표면크랙, 내부에서 개시하는 내부크랙으로 나눌 수 있고, 이들 크랙은 철도의 안전운행에 위험요소가 된다. 그러므로 이들 크랙의 성장수명 판단은 매우 중요하다. 본 연구에서는 철도차륜의 표면크랙 및 내부크랙의 응력분포상태, 변위 및 성장수명을 연구하였다. 특히 내부 및 표면크랙에 대해 유한요소해석을 실시하여 크랙선단의 응력상태, 변위, 응력확대계수를 찾아내었고, 이를 바탕으로 Paris 공식을 사용하여 성장수명을 예측하였다.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 춘계학술대회 논문집
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• pp.160-165
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• 2004

At this research, we examined probability of light bulb's life span value and prediction on purpose to inquire out the span of repeat velocity as fracture probability by executing the fatigue test, which is considered property of Tungsten filament's thermal fatigue used as an automobile bulb. As a result we can confirm what the most suitable solution is weibull distribution and log normal distribution. Tungsten filament's span gets longer as the fatigue repeat velocity gets shorter And, repeat span is about 15%~40% shorter than sequence life span.

• Steel and Composite Structures
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• 제18권3호
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• pp.547-563
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• 2015

Deflection in a beam of a composite frame is a serviceability design criterion. This paper presents a methodology for rapid prediction of long-term mid-span deflections of beams in composite frames subjected to service load. Neural networks have been developed to predict the inelastic mid-span deflections in beams of frames (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage in concrete) from the elastic moments and elastic mid-span deflections (neglecting cracking, and time effects). These models can be used for frames with any number of bays and stories. The training, validating, and testing data sets for the neural networks are generated using a hybrid analytical-numerical procedure of analysis. Multilayered feed-forward networks have been developed using sigmoid function as an activation function and the back propagation-learning algorithm for training. The proposed neural networks are validated for an example frame of different number of spans and stories and the errors are shown to be small. Sensitivity studies are carried out using the developed neural networks. These studies show the influence of variations of input parameters on the output parameter. The neural networks can be used in every day design as they enable rapid prediction of inelastic mid-span deflections with reasonable accuracy for practical purposes and require computational effort which is a fraction of that required for the available methods.

• Smart Structures and Systems
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• 제14권5호
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• pp.785-809
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• 2014

In this paper, an Adaptive nerou-based inference system (ANFIS) is being used for the prediction of shear strength of high strength concrete (HSC) beams without stirrups. The input parameters comprise of tensile reinforcement ratio, concrete compressive strength and shear span to depth ratio. Additionally, 122 experimental datasets were extracted from the literature review on the HSC beams with some comparable cross sectional dimensions and loading conditions. A comparative analysis has been carried out on the predicted shear strength of HSC beams without stirrups via the ANFIS method with those from the CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 - 94 codes of design. The shear strength prediction with ANFIS is discovered to be superior to CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 - 94. The predictions obtained from the ANFIS are harmonious with the test results not accounting for the shear span to depth ratio, tensile reinforcement ratio and concrete compressive strength; the data of the average, variance, correlation coefficient and coefficient of variation (CV) of the ratio between the shear strength predicted using the ANFIS method and the real shear strength are 0.995, 0.014, 0.969 and 11.97%, respectively. Taking a look at the CV index, the shear strength prediction shows better in nonlinear iterations such as the ANFIS for shear strength prediction of HSC beams without stirrups.

• Computers and Concrete
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• 제7권1호
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• pp.1-16
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• 2010

A simplified method, developed from the softened strut-and-tie model, for determining the mid-span deflection of deep beams at ultimate state is proposed. The mid-span deflection and shear strength predictions of the proposed model are compared with the experimental data collected from 70 simply supported reinforced concrete deep beams, loaded with concentrated loads located at a distance a from an end reaction. The comparison shows that the proposed model can accurately predict the mid-span deflection and shear strength of deep beams with different shear span-to-depth ratios, different concrete strengths, and different horizontal and vertical hoops.

• Computers and Concrete
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• 제19권3호
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• pp.293-303
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• 2017

Maximum deflection in a beam is a serviceability design criterion and occurs generally at or close to the mid-span. This paper presents a methodology using neural networks for rapid prediction of mid-span deflections in reinforced concrete beams subjected to service load. The closed form expressions are further obtained from the trained neural networks. The closed form expressions take into account cracking in concrete at in-span and at near the interior supports and tension stiffening effect. The expressions predict the inelastic deflections (incorporating the concrete cracking) from the elastic moments and the elastic deflections (neglecting the concrete cracking). Five separate neural networks are trained since these have been postulated to represent all beams having any number of spans. The training, validating, and testing data sets for the neural networks are generated using an analytical-numerical procedure of analysis. The proposed expressions have been verified by comparison with the experimental results reported elsewhere and also by comparison with the finite element method (FEM). The proposed expressions, at minimal input data and minimal computation effort, yield results that are close to FEM results. The expressions can be used in every day design since the errors are found to be small.

• Steel and Composite Structures
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• 제48권6호
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• pp.667-679
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• 2023

Concrete filled rectangular steel tubular (CFRST) composite truss girder is composed of the CFRST truss and concrete slab. The failure mechanism of the girder was different under bending and shear failure modes. The bending and shear strength of the girder were investigated experimentally. The influences of composite effect and shear to span ratio on failure modes of the girder was studied. Results indicated that the top chord and the joint of the truss were strengthened by the composited effect. The failure modes of the specimens were changed from the joint on top chord to the bottom chord. However, the composite effect had limited effect on the failure modes of the girder with small shear to span ratio. The concrete slab and top chord can be regarded as the composite top chord. In this case, the axial force distribution of the girder was close to the pin-jointed truss model. An approach of strength prediction was proposed which can take the composite effect and shear to span ratio into account. The approach gave accurate predictions on the strength of CFRST composite truss girder under different bending and shear failure modes.

• Smart Structures and Systems
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• 제16권3호
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• pp.497-519
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• 2015

This study discusses the use of Adaptive-Network-Based-Fuzzy-Inference-System (ANFIS) in predicting the shear strength of reinforced-concrete deep beams. 139 experimental data have been collected from renowned publications on simply supported high strength concrete deep beams. The results show that the ANFIS has strong potential as a feasible tool for predicting the shear strength of deep beams within the range of the considered input parameters. ANFIS's results are highly accurate, precise and therefore, more satisfactory. Based on the Sensitivity analysis, the shear span to depth ratio (a/d) and concrete cylinder strength ($f_c^{\prime}$) have major influence on the shear strength prediction of deep beams. The parametric study confirms the increase in shear strength of deep beams with an equal increase in the concrete strength and decrease in the shear span to-depth-ratio.