• 한국컴퓨터정보학회논문지
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• 제22권7호
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• pp.31-37
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• 2017

There are lots of combined battlefield elements which complete the war. It looks problematic when collecting and analyzing these elements and then predicting the situation of war. Commander's experience and military power assessment have widely been used to come up with these problems, then simulated combat training program recently supplements the war-game models through recording real-time simulated combat data. Nevertheless, there are challenges to assess winning factors of combat. In this paper, we characterize the combat element (ce) by clustering simulated combat data, and then suggest multi-layered artificial neural network (ANN) model, which can comprehend non-linear, cross-connected effects among ces to assess mission completion degree (MCD). Through our ANN model, we have the chance of analyzing and predicting winning factors. Experimental results show that our ANN model can explain MCDs through networking ces which overperform multiple linear regression model. Moreover, sensitivity analysis of ces will be the basis of predicting combat situation.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2002년도 봄 학술발표회 논문집
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• pp.300-307
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• 2002

Damage estimation methods are classified into two groups according to the dependence on the FE model : signal-based and model-based methods. Signal-based damage estimation methods are generally appropriate for detection of damage location, whereas not effective for estimation of damage severities. Model-based damage estimation methods are difficult to apply directly to the structures with a large number of the probable damaged members. It is difficult to obtain the exact model representing the real bridge behavior due to the modeling errors. The modeling errors even may exceed the modal sensitivity on damage. In this study, Model-based damage detection method which can effectively consider the modeling errors is suggested. Two numerical example analyses on a simple beam and a multi-girder bridge are presented to demonstrate the effectiveness of the presented method.

• Nuclear Engineering and Technology
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• 제53권3호
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• pp.752-762
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• 2021

The Smoothed Particle Hydrodynamics is one of the most widely used mesh-free numerical method for thermo-fluid dynamics. Due to its Lagrangian nature and simplicity, it is recently gaining popularity in simulating complex physics with large deformations. In this study, the 3D single/two-phase numerical simulations are performed on the Liquid Metal Reactor (LMR) centralized sloshing benchmark experiment using the SPH parallelized using a GPU. In order to capture multi-phase flows with a large density ratio more effectively, the original SPH density and continuity equations are re-formulated in terms of the normalized-density. Based upon this approach, maximum sloshing height and arrival time in various experimental cases are calculated by using both single-phase and multi-phase SPH framework and the results are compared with the benchmark results. Overall, the results of SPH simulations show excellent agreement with all the benchmark experiments both in qualitative and quantitative manners. According to the sensitivity study of the particle-size, the prediction accuracy is gradually increasing with decreasing the particle-size leading to a higher resolution. In addition, it is found that the multi-phase SPH model considering both liquid and air provides a better prediction on the experimental results and the reality.

• 한국안전학회지
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• 제38권3호
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• pp.110-120
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• 2023

Following the Fukushima accident, portable equipment employed as accident mitigating systems have been installed and operated to reduce core damage and large early release frequencies. In addition, the establishment of an accident management strategy has gained importance. This study investigated the current status of portable equipment including the international portable equipment FLEX (diverse and flexible coping strategies), and domestic portable equipment multi-barrier accident coping strategy (MACST). Research on optimal utilization of MACST remains insufficient. As a preliminary study for establishing an optimal strategy, sensitivity studies were conducted to facilitate the priority of use on portable equipment, number of portable equipment, and dependency of operator actions based on a multi-unit probabilistic safety assessment model. The results revealed the conditions that reduced the multi-unit and site conditional core damage probabilities, indicating the optimal strategy of MACST. The results of this study can be used as a reference for establishing an optimal strategy that utilizes domestic safety equipment in the future.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.297-301
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• 2009

The impact of adding additional Multi-Criteria Decision Analysis (MCDA) criteria is demonstrated because current research shows MCDA for flood damage has been applied using only a few criteria but for better results the MCDA approach needs to apply more criteria for evaluating the alternatives. By adding additional criteria into MCDA, the capability to make the best alternatives more diverse and show the decision maker more differences in the scores of the alternatives to allow the decision maker to discriminate is significantly improved. The target region for a demonstration application of the methodology was the Suyoung River Basin in Korea. The 1991 Gladys flood event and five different return periods were used as a case study to demonstrate the proposed methodology of evaluation of various flood damage reduction alternatives.

• Steel and Composite Structures
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• 제29권6호
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• pp.771-783
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• 2018

In this paper, topology optimization (TO) is applied to find a new configuration for the perforated steel plate shear wall (PSPSW) based on the maximization of reaction forces as the objective function. An infill steel plate is introduced based on an experimental model for TO. The TO is conducted using the sensitivity analysis, the method of moving asymptotes and SIMP method. TO is done using a nonlinear analysis (geometry and material) considering the buckling. The final area of the optimized plate is equal to 50% of the infill plate. Three plate thicknesses and three length-to-height ratios are defined and their effects are investigated in the TO. It indicates the plate thickness has no significant impact on the optimization results. The nonlinear behavior of optimized plates under cyclic loading is studied and the strength, energy and fracture tendency of them are investigated. Also, four steel plates including infill plate, a plate with a central circle and two types of the multi-circle plate are introduced with equal plate volume for comparing with the results of the optimized plate.

• Computers and Concrete
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• 제30권5호
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• pp.301-310
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• 2022

Cement-based sensors have been widely used as structural health monitoring systems, however, their long-term sensing performance have not actively investigated. In this study, a deep learning-based methodology is adopted to predict the long-term piezoresistive properties of cement-based sensors. Samples with different multi-walled carbon nanotube contents (0.1, 0.3, and 0.5 wt.%) are fabricated, and piezoresistive tests are conducted over 10,000 loading cycles to obtain the training data. Time-dependent degradation is predicted using a modified long short-term memory (LSTM) model. The effects of different model variables including the amount of training data, number of epochs, and dropout ratio on the accuracy of predictions are analyzed. Finally, the effectiveness of the proposed approach is evaluated by comparing the predictions for long-term piezoresistive sensing performance with untrained experimental data. A sensitivity of 6% is experimentally examined in the sample containing 0.1 wt.% of MWCNTs, and predictions with accuracy up to 98% are found using the proposed LSTM model. Based on the experimental results, the proposed model is expected to be applied in the structural health monitoring systems to predict their long-term piezoresistice sensing performances during their service life.

• Structural Engineering and Mechanics
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• 제63권4호
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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.

• 지구물리와물리탐사
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• 제23권1호
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• pp.38-49
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• 2020

음향파 완전파형역산은 탄성파 탐사를 통해 얻은 관측자료와 음향파 모델링자료를 맞춤으로써 지층의 속도모델을 고해상도로 구축하는 최적화 과정이다. 기존의 스트리머를 이용한 해양 탄성파 탐사 자료에 대한 음향파 완전파형역산에서는 압력자료만을 활용하여 P파 속도모델을 구축한다. 그러나 최근 다성분 해저면 탄성파 탐사기술의 발달로 다성분자료의 취득 사례가 늘고 있으며, 이에 따라 해양에서 얻어지는 다성분 자료를 활용한 음향파 완전파형역산 기법에 대한 연구가 필요하다. 이 연구에서는 수평 및 수직 입자가속도 자료를 활용한 효과적인 음향파 완전파형역산 전략을 제시하고자 한다. 이를 위해, 우선 음향파 모델링으로 제작된 압력 및 입자가속도 자료와 민감도커널을 분석하여 파형역산 과정에서 각 자료의 성분별 특성을 관찰하였다. 압력 자료에 함께 나타났던 직접파, 다이빙파 및 반사파가 수직 및 수평 입자가속도 자료에서 파동의 진행방향에 따라 분리되어 나타나는 것을 확인하였으며, 수평 입자가속도 자료는 상부의 장파장구조를, 수직 입자가속도 자료는 하부의 장파장구조와 전체 영역에서의 단파장구조를 구축하는 데 효과적임을 확인할 수 있었다. 이러한 분석 결과를 바탕으로 입자가속도 자료만을 활용해 음향파 완전파형역산을 수행하는 순차적 자료 활용전략을 제시하며, 압력자료를 얻지 못하였거나 품질이 낮은 경우에도 입자가속도 자료를 활용하는 음향파 완전파형역산을 통해 양호한 P파 속도모델을 구축할 수 있을 것으로 기대된다.

• 한국BIM학회 논문집
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• 제11권1호
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• pp.39-46
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• 2021

Urban residents in crowded complexes are making increasing civil complaints about noise and demanding pleasant and comfortable residential environments. Because noise is one of the most important factors related to urban residents' dissatisfaction with their living environments, the present study investigates the direct and indirect effects of noise-related outdoor environmental factors on residential level satisfaction, using noise level data from 29 noise-measuring stations in Seoul. From 62 multi-family apartment complexes near these stations, the authors collected GIS-based environmental attribute data, as well as survey data including the residents' personal characteristics and indicators designed to measure latent psychological characteristics: noise sensitivity and residential noise level satisfaction. This study then utilized structural equation models to analyze the direct variables influencing the latent variables of noise sensitivity and residential noise level satisfaction, as well as the complex relationships among all variables. The result showed that residents who are exposed to less noise, possibly due to living in apartments facing relatively quiet roads, protected by soundproof walls, or surrounded by densely planted trees, tend to be less noise sensitive, which makes them more satisfied with the ambient noise level. Therefore, critical outdoor environmental variables can be used to reduce noise sensitivity and improve residential noise level satisfaction.