• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2011년도 정기 학술대회
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• pp.265-268
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• 2011

하중 속도에 따른 콘크리트 재료의 역학적 특성은 구조물의 동적파괴거동에 영향을 미친다. 본 연구는, rigid-body-spring network를 이용하여 파괴해석을 수행하고, 거시적 시뮬레이션에서 속도효과를 표현하기 위하여 점소성 파괴모델을 적용하였다. 보정을 위해서 Perzyna 구성관계식의 점소성 계수들이 다양한 하중속도에 따른 직접인장실험을 통해서 결정되었다. 동정상승계수를 이용하여 하중 속도가 증가함에 따른 강도 증가를 표현하였고 이를 실험결과와 비교하였다. 다음으로 느린 하중속도와 빠른 하중속도에 따라 단순 콘크리트 보와 철근 콘크리트 보에 대한 휨 실험을 수행하였으며, 하중 속도에 따라서 서로 다른 균열 패턴을 관찰할 수 있었다. 빠른 하중은 보의 파괴가 국부적으로 나타나게 만드는데, 이는 속도 의존적 재료의 특성 때문이다. 구조적인 측면에서, 보강재는 느린 하중속도에서 균열의 크기를 줄이고 연성을 높이는 데 큰 영향을 미친다. 본 논문은 속도 의존적 거동에 대한 이해와 동적하중에 대한 보강효과를 제시한다.

• Computers and Concrete
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• 제12권2호
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• pp.229-241
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• 2013

The objective of this study is to reveal the sufficiency of neural networks (NN) as a securer, quicker, more robust and reliable method to be used in seismic assessment of existing reinforced concrete buildings. The NN based approach is applied as an alternative method to determine the seismic performance of each existing RC buildings, in terms of damage level. In the application of the NN, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm is employed using a scaled conjugate gradient. NN based model wasd eveloped, trained and tested through a based MATLAB program. The database of this model was developed by using a statistical procedure called P25 method. The NN based model was also proved by verification set constituting of real existing RC buildings exposed to 2003 Bingol earthquake. It is demonstrated that the NN based approach is highly successful and can be used as an alternative method to determine the seismic performance of each existing RC buildings.

• Structural Engineering and Mechanics
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• 제64권3호
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• pp.339-346
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• 2017

Slabs prevent harmful effects of fire that may occur in any floor. However, it is necessary to protect the slabs from fire. Insulation materials may be appropriate to protect reinforced concrete (RC) slab from elevated temperature. In the present study, a model has been developed in artificial neural network (ANN) to predict the moment capacity ($M_r$) of RC slabs exposed to fire with insulation material. 672 data were obtained for ANN model through author's prepared program. Input layer in model consisted of seven input parameters; such as effective depth (d), ratio of d'/d, thermal conductivity coefficient ($k_{insulation}$), insulation materials thickness ($L_{insulation}$), reinforcement area ($A_{st}$), fire exposure time ($t_{\exp}$), and concrete compressive strength ($f_c$). The predicted $M_r$ by ANN was consistent with the obtained $M_r$ by author. It is proposed to ease computational complexity in determining $M_r$ using ANN. The effects of using insulation material on the moment capacity in RC slabs were also investigated. Insulating material with low thermal conductivity has been found to be more effective for durability to high temperature.

• Earthquakes and Structures
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• 제25권5호
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• pp.385-398
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• 2023

The parameters of the multiple-vertical-line-element model (MVLEM) of reinforced concrete (RC) shear walls are often empirically determined, which causes large simulation errors. To improve the simulation accuracy of the MVLEM for RC shear walls, this paper proposed a novel method to determine the MVLEM parameters using the artificial neural network (ANN). First, a comprehensive database containing 193 shear wall specimens with complete parameter information was established. And the shear walls were simulated using the classic MVLEM. The average simulation errors of the lateral force and drift of the peak and ultimate points on the skeleton curves were approximately 18%. Second, the MVLEM parameters were manually optimized to minimize the simulation error and the optimal MVLEM parameters were used as the label data of the training of the ANN. Then, the trained ANN was used to generate the MVLEM parameters of the collected shear walls. The results show that the simulation error of the predicted MVLEM was reduced to less than 13% from the original 18%. Particularly, the responses generated by the predicted MVLEM are more identical to the experimental results for the testing set, which contains both flexure-control and shear-control shear wall specimens. It indicates that establishing MVLEM for RC shear walls using ANN is feasible and promising, and that the predicted MVLEM substantially improves the simulation accuracy.

• Earthquakes and Structures
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• 제15권4호
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• pp.431-441
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• 2018

This study provides fragility-based assessment of seismic performance of reinforced concrete bridges. Seismic fragility curves were created using nonlinear analysis (NA) and artificial neural networks (ANNs). Nonlinear response history analyses were performed, in order to calculate the seismic performances of the bridges. To this end, 306 bridge-earthquake cases were considered. A multi-layered perceptron (MLP) neural network was implemented to predict the seismic performances of the selected bridges. The MLP neural networks considered herein consist of an input layer with four input vectors; two hidden layers and an output vector. In order to train ANNs, 70% of the numerical results were selected, and the remained 30% were employed for testing the reliability and validation of ANNs. Several structures of MLP neural networks were examined in order to obtain suitable neural networks. After achieving the most proper structure of neural network, it was used for generating new data. A total number of 600 new bridge-earthquake cases were generated based on neural simulation. Finally, probabilistic seismic safety analyses were conducted. Herein, fragility curves were developed using numerical results, neural predictions and the combination of numerical and neural data. Results of this study revealed that ANNs are suitable tools for predicting seismic performances of RC bridges. It was also shown that yield stresses of the reinforcements is one of the important sources of uncertainty in fragility analysis of RC bridges.

• Computers and Concrete
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• 제28권6호
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• pp.635-642
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• 2021

In this paper, soft learning techniques are used to predict the ultimate torsional capacity of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. Soft computing techniques, namely Artificial Neural Network, trained by various back propagation algorithms, and Particle Swarm Optimization (PSO) algorithm, have been used to model and predict the torsional strength of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. The performance of each model has been evaluated by using statistical parameters such as coefficient of determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The hybrid PSO NN model resulted in an R² of 0.9292 with an RMSE of 5.35 for training and an R² of 0.9328 with an RMSE of 4.57 for testing. Another model, ANN BP, produced an R² of 0.9125 with an RMSE of 6.17 for training and an R² of 0.8951 with an RMSE of 5.79 for testing. The results of the PSO NN model were in close agreement with the experimental values. Thus, the PSO NN model can be used to predict the ultimate torsional capacity of RC beams strengthened with FRP with greater acceptable accuracy.

• Computers and Concrete
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• 제23권1호
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• pp.49-60
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• 2019

In recent years, multiple experimental studies have been performed on using fiber reinforced polymer (FRP) bars in reinforced concrete (RC) structural members. FRP bars provide a new type of reinforcement that avoids the corrosion of traditional steel reinforcement. In this study, predicting the shear strength of RC beams with FRP longitudinal bars using artificial neural networks (ANNs) is investigated as a different approach from the current specific codes. An ANN model was developed using the experimental data of 104 FRP-RC specimens from an existing database in the literature. Seven different input parameters affecting the shear strength of FRP bar reinforced RC beams were selected to create the ANN structure. The most convenient ANN algorithm was determined as traingdx. The results from current codes (ACI440.1R-15 and JSCE) and existing literature in predicting the shear strength of FRP-RC beams were investigated using the identical test data. The study shows that the ANN model produces acceptable predictions for the ultimate shear strength of FRP-RC beams (maximum $R^2{\approx}0.97$). Additionally, the ANN model provides more accurate predictions for the shear capacity than the other computed methods in the ACI440.1R-15, JSCE codes and existing literature for considering different performance parameters.

• 한국철도학회논문집
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• 제19권3호
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• pp.314-323
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• 2016

직류 전철변전소의 가선전압은 전동차들의 회생제동 및 역행가속패턴에 따라 급격히 상승 또는 하강하는 특성을 갖는다. 가선전압 순시 변동폭을 최소로 유지함으로써, 전철변전소와 전동차들의 에너지 효율을 개선시키기 위한 다양한 연구들이 이루어지고 있다. 본 논문은 직류전철 변전소의 가선전압의 급격한 변동특성을 모델링하고 선형인공 신경망 알고리즘을 이용한 가선전압 회로모델의 파라메터 추정 방법을 제안하며, 최소자승법을 이용한 추정방법과의 비교를 통해 이 방법의 타당성을 입증한다. 가선전압 및 피더전류들의 누적 측정값을 사용하여 일괄처리 최소자승법으로 RC 병렬회로의 파라메터들을 추정한 결과를 제시하며, 실시간 가선전압 및 피더전류 측정값을 이용하여 오차역 전파방식으로 학습되는 선형인공신경망 기법 추정 결과를 분석한다.

• 대한전자공학회논문지SD
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• 제47권12호
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• pp.68-74
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• 2010

본 논문은 Ultra-Low-Power (ULP) Radoi를 위한 Sub-${\mu}$W 급 저 전력 발진기 회로에 관한 것이다. 저 전력 발진기의 구조로서 Relaxation 구조와 Wien-Bridge 구조의 시뮬레이션 비교를 통하여, 소모 전류의 최소화 및 저 전력 동작에 최적인 Wien-Bridge 구조를 선택 하였다. Wien-Bridge 발진기 회로는 폐쇄 루프 이득이 ($1+R_2/R_1$) 인 비반전 OPAMP 증폭회로에 부귀환 경로로 RC 회로망이 접속 되어 있다. 이 회로망의 RC값과 증폭기의 폐쇄 루프 이득에 의해 발진 주파수가 정해지게 된다. 본 연구에서는 루프 이득 조정을 위해 일반적으로 사용하는 가변저항대신, MIM 커패시터와 MOS 버랙터를 조합한 가변 커패시터를 사용하여, 발진기의 폐쇄 루프 이득을 적절히 조절 하는 방식을 제안하고 이를 구현하였다. 폐쇄 루프 이득을 안정적으로 조절 할 수 있음에 따라 발진기 출력의 안정화를 얻을 수 있으며, 출력신호의 비선형성도 개선 할 수 있다. $0.18{\mu}m$ CMOS 공정을 이용해 구현된 발진기는 22 kHz 출력주파수에서 560 nA의 전류를 소모한다.

• IMMUNE NETWORK
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• 제4권2호
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• pp.100-107
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• 2004

Background: The fruit of Rubus coreanus (RC), a perennial herb, has been cultivated for a long time as a popular vegetable. The anti-allergy mechanism of RC is unknown. The purpose of this study is to investigate the inhibitory effect of RC on compound 48/80- or anti-DNP IgE-induced mast cell activation. Methods: For this, influences of RC on the compound 48/80-induced degranulation, histamine release, calcium influx and the change of the intracellular cAMP (cyclic adenosine-3',5' monophosphate) levels of rat peritoneal mast cells (RPMC) and on the anti-DNP IgE-induced histamine release of RPMC were observed. Results: The pretreatment of RC inhibited compound 48/80-induced degranulation, histamine release and intracelluar calcium uptake of RPMC. The anti-DNP IgE-induced histamine release of RPMC was significantly inhibited by pretreatment of RC. The RC increased the level of intracellular cAMP of RPMC, and the pretreatment of RC inhibited compound 48/80-induced decrement of intracellular cAMP of RPMC. Conclusion: These results suggest that RC contains some substances with an activity to inhibit the compound 48/80- or anti-DNP IgE-induced mast cell activitation. The inhibitory effects of RC are likely due to the stabilization of mast cells by blocking the calcium uptake and enhancing the level of intracellular cAMP.