• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.362-364
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• 1992

In this paper, the development of a fiber optic network for an integrated digital protection relay system is described. The structure of the developed network is determined to loosen the optic requirements and to have good extensibility while providing sufficient functions for protection and control for substations. The network has a hierarchical structure with two levels. The upper level handles data for monitoring and control of the system with star topology. The lower level manages the real time data for bus protection with one-to-one connections. Communication flows of each level are described. The HDLC is employed as the network protocol.

• Advances in concrete construction
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• v.3 no.2
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• pp.91-102
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• 2015

In this paper, the load-deflection analysis of the Carbon Fiber Reinforced Polymer (CFRP) strengthened Reinforced Concrete (RC) slab using Recurrent Neural Network (RNN) is investigated. Six reinforced concrete slabs having dimension $1800{\times}400{\times}120mm$ with similar steel bar of 2T10 and strengthened using different length and width of CFRP were tested and compared with similar samples without CFRP. The experimental load-deflection results were normalized and then uploaded in MATLAB software. Loading, CFRP length and width were as neurons in input layer and mid-span deflection was as neuron in output layer. The network was generated using feed-forward network and a internal nonlinear condition space model to memorize the input data while training process. From 122 load-deflection data, 111 data utilized for network generation and 11 data for the network testing. The results of model on the testing stage showed that the generated RNN predicted the load-deflection analysis of the slabs in acceptable technique with a correlation of determination of 0.99. The ratio between predicted deflection by RNN and experimental output was in the range of 0.99 to 1.11.

• Structural Engineering and Mechanics
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• v.58 no.3
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• pp.459-473
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• 2016

Various vague and unstructured problems encountered the civil engineering/designers that persuaded by their experiences. One of these problems is the structural failure of the reinforced concrete (RC) building determination. Typically, using the traditional Limit state method is time consuming and complex in designing structures that are optimized in terms of one/many parameters. Recent research has revealed the Artificial Neural Networks potentiality in solving various real life problems. Thus, the current work employed the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifier to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. In order to evaluate the proposed method performance, a database of 257 multistoried buildings RC structures has been constructed by professional engineers, from which 150 RC structures were used. From the structural design, fifteen features have been extracted, where nine features of them have been selected to perform the classification process. Various performance measures have been calculated to evaluate the proposed model. The experimental results established satisfactory performance of the proposed model.

• Structural Engineering and Mechanics
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• v.63 no.4
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• pp.429-438
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• 2017

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.227-236
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• 2002

The purpose of this study makes a retrofit and rehabilitation practice trough the analysis and the improvement for the underlying problem of current retrofit and rehabilitation methods. Therefore, the deterioration process, the damage cause, the condition classification, the fatigue mechanism and the applied quantity of strengthening methods for RC deck slabs were analyzed. Artificial neural networks are efficient computing techniques that are widely used to solve complex problems in many fields. In this study, a back-propagation neural network model for estimating a management on existing reinforced concrete bridge decks from damage cause, damage type, and integrity assessment at the initial stage is need. The training and testing of the network were based on a database of 36. Four different network models were used to study the ability of the neural network to predict the desirable output of increasing degree of accuracy. The neural networks is trained by modifying the weights of the neurons in response to the errors between the actual output values and the target output value. Training was done iteratively until the average sum squared errors over all the training patterns were minimized. This generally occurred after about 5,000 cycles of training.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.1254-1256
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• 2001

This paper composes the Electric Railway System with the common grounding based on the 5-port network model. We compare the magnification ratio of harmonic currents according to locations of RC bank(i.e s/s. sp, and pp). It takes a lot of costs to equip the RC-bank at all location. And it is NOT effective that RC-bank is equipped at S/S. Finally, this paper proposes SP as the optimal site of RC-bank aspect reducing harmonic.

• Journal of Korean Association for Spatial Structures
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• v.23 no.2
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• pp.29-36
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• 2023

Reinforcement learning (RL) is widely applied to various engineering fields. Especially, RL has shown successful performance for control problems, such as vehicles, robotics, and active structural control system. However, little research on application of RL to optimal structural design has conducted to date. In this study, the possibility of application of RL to structural design of reinforced concrete (RC) beam was investigated. The example of RC beam structural design problem introduced in previous study was used for comparative study. Deep q-network (DQN) is a famous RL algorithm presenting good performance in the discrete action space and thus it was used in this study. The action of DQN agent is required to represent design variables of RC beam. However, the number of design variables of RC beam is too many to represent by the action of conventional DQN. To solve this problem, multi-agent DQN was used in this study. For more effective reinforcement learning process, DDQN (Double Q-Learning) that is an advanced version of a conventional DQN was employed. The multi-agent of DDQN was trained for optimal structural design of RC beam to satisfy American Concrete Institute (318) without any hand-labeled dataset. Five agents of DDQN provides actions for beam with, beam depth, main rebar size, number of main rebar, and shear stirrup size, respectively. Five agents of DDQN were trained for 10,000 episodes and the performance of the multi-agent of DDQN was evaluated with 100 test design cases. This study shows that the multi-agent DDQN algorithm can provide successfully structural design results of RC beam.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.676-679
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• 2011

인공신경망(Artificial Neural Network)을 이용하여 RC Mock-up 구조물의 손상위치 및 손상정도를 단계적으로 추정하였다. 대상 구조물은 가진실험을 통하여 구조물의 응답을 취득하고 구조물식별기법(Structural System Identification)을 통하여 구조물의 동특성을 찾았다. 유한요소해석프로그램을 사용하여 동특성이 계측치와 가장 유사한 기본해석모델을 만든 후 이 기본해석모델을 이용하여 학습데이터를 생성하였다. 기존 인공신경망을 이용한 손상탐지를 개선하고자 본 연구에서는 인공신경망 학습데이터를 분석하였고 효과적인 손상탐지를 위하여 학습데이터를 가공하였다. 가공된 학습데이터를 사용하여 단계별 손상탐지를 실시하였고 기존 손상탐지 방법보다 좋은 결과를 유도하였다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.687-691
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• 2010

구조물의 건전도를 평가하기 위해 상시 구조물 계측을 이용한 Structural Health Monitoring (SHM) 시스템을 적용하게 된다. SHM 시스템의 궁극적 목적은 계측된 데이터를 이용하여 구조물의 손상위치 및 손상정도를 분석하여 거주자에게 유지관리정보와 대처요령 신속하게 제공하는 것이다. 따라서 본 연구에서는 구조물의 손상탐지를 위해 인공신경망(Artificial Neural Network)을 도입한 알고리즘을 수립하고, 이를 3층 실대 RC Mock-up 구조물에 적용하여 성능을 평가하였다. 먼저 인공신경망의 학습을 위해 구조해석 프로그램을 이용하여 구조물의 손상에 따른 동적특성 변화 데이터베이스를 구축하였다. 그리고 학습된 인공망에 실제 구조물에서 추출한 동특성의 변화를 입력하여 손상탐지를 실시하였다. 이를 통해 인공신경망의 학습방법, 학습데이터의 정규화 방법 등을 규명하고 인공신경망을 이용한 손상탐지의 효과를 분석하였다.

• Structural Engineering and Mechanics
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• v.59 no.4
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• pp.653-669
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• 2016

Recent earthquakes worldwide show that a significant portion of the earthquake shaking happens in the vertical direction. This phenomenon has raised significant interests to consider the vertical ground motion during the seismic design and assessment of the structures. Strong vertical ground motions can alter the axial forces in the columns, which might affect the shear capacity of reinforced concrete (RC) members. This is particularly important for non-ductile RC frames, which are very vulnerable to earthquake-induced collapse. This paper presents the detailed nonlinear dynamic analysis to quantify the collapse risk of non-ductile RC frame structures with varying heights. An array of non-ductile RC frame architype buildings located in Los Angeles, California were designed according to the 1967 uniform building code. The seismic responses of the architype buildings subjected to concurrent horizontal and vertical ground motions were analyzed. A comprehensive array of ground motions was selected from the PEER NGA-WEST2 and Iran Strong Motions Network database. Detailed nonlinear dynamic analyses were performed to quantify the collapse fragility curves and collapse margin ratios (CMRs) of the architype buildings. The results show that the vertical ground motions have significant impact on both the local and global responses of non-ductile RC moment frames. Hence, it is crucial to include the combined vertical and horizontal shaking during the seismic design and assessment of non-ductile RC moment frames.