• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.348-348
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• 2021

가뭄 연구의 궁극적 목표는 가뭄 발생의 메커니즘에 대한 이해를 높이고, 예측기술을 향상시켜 선제적 대응이 가능하도록 하는 것이다. 일반적으로 가뭄분석에 활용되는 가뭄지표는 연속형 변수로 간주하여 확률모형을 구축하지만, 가뭄상태와 가뭄피해 자료는 순서형 및 이산형 변수이므로 범주형 자료 분석 기법을 적용하는 것이 더 적절하다. 따라서 본 연구에서는 기상학적 가뭄과 피해발생 사이의 관계를 규명하기 위해 범주형 자료 분석 방법 중 로그선형(log-linear) 모형과 로지스틱(logistic) 회귀모형을 활용하였다. 가뭄피해 예측을 위한 가뭄 피해 정보를 수집하는 것은 매우 어려운 일이다. 가뭄의 영향으로 인해 발생할 수 있는 피해의 종류가 다양하며, 여러 분야의 이해관계자가 받아들이는 가뭄의 피해 양상이 다르기 때문이다. 본 연구에서는 국가가뭄정보포털(drought.go.kr)에서 충청북도의 가뭄피해현황 자료를 수집하였다. 30년(1991~2020년)동안 238개 읍면동 중 34개 행정구역에서 총 272건의 가뭄피해가 발생한 것으로 확인되었다. 표준강수지수(SPI)를 이용하여 분석된 지역별 연평균 가뭄발생횟수는 약 8.44회이며, 가뭄이 가장 많이 발생한 해는 2001년(평균 가뭄발생 18.7회)이었다. 강수의 부족으로 인해 발생하는 기상학적 가뭄이 사회경제적 피해를 야기하는 수문학적 가뭄으로 전이되기까지 몇 주에서 몇 달까지 시간이 소요된다. 이러한 관계를 파악하기 위해 가뭄피해 발생 여부를 예측변수, 가뭄피해 발생 이전의 가뭄상태를 설명변수로 설정하여 기상학적 가뭄 발생에 따른 가뭄피해 발생 확률을 산정하였다. 그 결과 가뭄피해 발생 당시의 가뭄상태보다 그 이전에 연속된 가뭄상태가 있을 경우 가뭄피해 발생 확률이 약 2.5배 상승하는 것으로 나타났다.

• Journal of Korea Water Resources Association
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• v.56 no.1
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• pp.45-52
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• 2023

Due to natural disasters such as heavy rain that occurred in the metropolitan area in August 2022, human casualties and property damage are increasing. Accordingly, the government is making efforts to respond to natural disasters, but due to the absence of related standards and standardized standards, problems such as increased construction costs and deterioration in construction quality for disaster prevention facility maintenance projects are occurring. Accordingly, a rough construction cost estimation module was developed and applied to 25 new pumping stations in Korea. As a result of the analysis, the accuracy of the rough construction cost derived through the module recorded 70% of the detailed design cost, which is 4% higher than the previously used rough construction cost accuracy of 66% by the Ministry of Environment. Accordingly, it is expected that the efficiency of the disaster prevention project can be increased if the developed module is used to calculate the rough construction cost for storm and flood disaster prevention in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.208-208
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• 2022

Recently, the frequency of abnormal weather due to complex factors such as global warming is increasing frequently. From the past rainfall patterns, it is evident that climate change is causing irregular rainfall patterns. This phenomenon causes difficulty in predicting rainfall and makes it difficult to prevent and cope with natural disasters, casuing human and property damages. Therefore, accurate rainfall estimation and rainfall occurrence time prediction could be one of the ways to prevent and mitigate damage caused by flood and drought disasters. However, rainfall prediction has a lot of uncertainty, so it is necessary to understand and reduce this uncertainty. In addition, when accurate rainfall prediction is applied to the rainfall-runoff model, the accuracy of the runoff prediction can be improved. In this regard, this study aims to increase the reliability of rainfall prediction by analyzing the uncertainty of the Korean rainfall ensemble prediction data and the outflow analysis model using the Limited Area ENsemble (LENS) and the Grid based Rainfall-runoff Model (GRM) models. First, the possibility of improving rainfall prediction ability is reviewed using the QM (Quantile Mapping) technique among the bias correction techniques. Then, the GRM parameter calibration was performed twice, and the likelihood-parameter applicability evaluation and uncertainty analysis were performed using R², NSE, PBIAS, and Log-normal. The rainfall prediction data were applied to the rainfall-runoff model and evaluated before and after calibration. It is expected that more reliable flood prediction will be possible by reducing uncertainty in rainfall ensemble data when applying to the runoff model in selecting behavioral models for user uncertainty analysis. Also, it can be used as a basis of flood prediction research by integrating other parameters such as geological characteristics and rainfall events.

• Wind and Structures
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• v.37 no.4
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• pp.289-302
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• 2023

Insulating glass units (IGUs) have been widely used in buildings in recent years due to their superior thermal insulation performance. However, because of the panel reciprocating motion and fatigue deterioration of sealants under long-term wind loads, many IGUs have the problem of early failure of watertight properties in real usage. This study aimed to propose a statistical method for wind-induced deflection of IGU panels during the whole life service period, for further precise analysis of the accumulated fatigue damage at the sealed part of the edge bond. By the estimation of the wind occurrence regularity based on wind pressure return period, the events of each wind speed interval during the whole life were obtained for the IGUs at 50m height in Beijing, which are in good agreement with the measured data. Also, the wind-induced deflection analysis method of IGUs based on the formula of airspace coefficient was proposed and verified as an improvement of the original stiffness distribution method with the average relative error compared to the test being about 3% or less. Combining the two methods above, the deformation of the outer and inner panes under wind loads during 30 years was precisely calculated, and the deflection and stress state at selected locations were obtained finally. The results show that the compression displacement at the secondary sealant under the maximum wind pressure is close to 0.3mm (strain 2.5%), and the IGUs are in tens of thousands of times the low amplitude tensile-compression cycle and several times to dozens of times the relatively high amplitude tensile-compression cycle environment. The approach proposed in this paper provides a basis for subsequent studies on the durability of IGUs and the wind-resistant behaviors of curtain wall structures.

• Journal of Radiation Protection and Research
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• v.48 no.4
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• pp.175-183
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• 2023

Background: Recently, biological adsorbents have been developed for removing radionuclides from radioactive liquid waste due to their high selectivity, eco-friendliness, and renewability. However, since they can be damaged by radiation in radioactive waste, a method for estimating the bio-adsorbent performance as a time should consider the radiation damages in terms of their renewability. This paper aims to develop a simulation method that applies a deep learning technique to rapidly and accurately estimate the adsorption performance of bio-adsorbents when inserted into liquid radioactive waste. Materials and Methods: A model that describes various interactions between a bio-adsorbent and liquid has been constructed using numerical methods to estimate the adsorption capacity of the bio-adsorbent. To generate datasets for machine learning, Monte Carlo N-Particle (MCNP) simulations were conducted while considering radioactive concentrations in the adsorbent column. Results and Discussion: Compared with the result of the conventional method, the proposed method indicates that the accuracy is in good agreement, within 0.99% and 0.06% for the R² score and mean absolute percentage error, respectively. Furthermore, the estimation speed is improved by over 30 times. Conclusion: Note that an artificial neural network can rapidly and accurately estimate the survival rate of a bio-adsorbent from radiation ionization compared with the MCNP simulation and can determine if the bio-adsorbents are reusable.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.2
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• pp.17-26
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• 2024

In recent times, accidents involving structural elements, formwork, and shore have been persistently occurring during concrete pouring, especially in multi-story reinforced concrete (RC) structures. In previous studies, research on construction load analysis was mainly conducted for cases where the thickness of all slabs is constant. However, when the thickness of some slabs is different, the variation in the stiffness of slab cross-sections can lead to different distributions of construction loads, necessitating further investigation. In this study, the slab thickness was set as a variable, and the analysis of the distribution of construction loads was conducted, taking into account the influence of changes in slab thickness on the concrete stiffness and structure. It was confirmed that not only the concrete material stiffness but also the slab cross-section stiffness should be considered in the estimation of construction loads when the slab thickness changes. As the slab thickness increases, the maximum construction load and maximum damage parameter on the layer with increased thickness significantly increase, and it was observed that a thicker slab results in a higher proportion of construction load.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.2
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• pp.115-124
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• 2024

To detect and prevent structural damage caused by various loads on marine structures and ships, structural health monitoring procedure is essential. Estimating loads acting on the structures which are measured by sensors that are mounted properly are crucial for structural health monitoring. However, attaching an excessive number of sensors to the structure without consideration can be inefficient due to the high costs involved and the potential for inducing structural instability. In this study, we introduce a method to determine the optimal number of sensors and their optimized locations for strain measurement sensors, allowing for accurate load estimation throughout the structure using model expansion method. To estimate the loads exerted on the entire structure with minimal sensors, we construct a strain-load interpolation matrix using the strain mode shapes of the finite element (FE) model and select the optimal sensor locations by applying D-Optimal Design and the row exchange algorithm. Finally, we estimate the loads exerted on the entire structure using the model expansion method. To validate the proposed method, we compare the results obtained by applying the optimal sensor placement and model expansion method to an FE model subjected to arbitrary loads with the loads exerted on the entire FE model, demonstrating efficiency and accuracy.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4367-4381
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• 2023

Nuclear power plants are increasingly being equipped with digital I&C systems. Although some probabilistic safety assessment (PSA) models for the digital I&C of nuclear power plants have been constructed, there is currently no specific internationally agreed guidance for their modeling. This paper presents an initiative by the OECD Nuclear Energy Agency called "Digital I&C PSA - Comparative application of DIGital I&C Modelling Approaches for PSA (DIGMAP)", which aimed to advance the field towards practical and defendable modeling principles. The task, carried out in 2017-2021, used a simplified description of a plant focusing on the digital I&C systems important to safety, for which the participating organizations independently developed their own PSA models. Through comparison of the PSA models, sensitivity analyses as well as observations throughout the whole activity, both qualitative and quantitative lessons were learned. These include insights on failure behavior of digital I&C systems, experience from models with different levels of abstraction, benefits from benchmarking as well as major contributors to the core damage frequency and those with minor effect. The study also highlighted the challenges with modeling of large common cause component groups and the difficulties associated with estimation of key software and common cause failure parameters.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.2
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• pp.155-163
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• 2007

High Strength Concrete (HSC) has weakness that in a fire, it is spalled and brittles. The phenomenon of spalling is made by water vapor's (resulting from evaporation in the material at over $100{^{\circ}C}$)' being confined in watertight concrete. As the concrete strength increases, the degree of damage caused by the spalling becomes more serious because of the permeability. It is reported that the polypropylene(PP) fiber has an important role in protecting concrete from spalling and the optimum dosage of PP fiber is 0.2%. This study was conducted on the nonreinforced concrete specimens. The high-temperature behavior of high-strength reinforced concrete columns with various concrete strength and various dosage of PP fibers was investigated in this study. The results show that the ratio of unstressed residual strength of columns increases as the concrete strength increases and the ratio of unstressed residual strength of columns increases as the dosage of PP fiber increases from 0% to 0.2%, however, the effect of fiber dosage on residual strength of column barely changes above 0.2%.

• Smart Structures and Systems
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• v.33 no.4
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• pp.291-300
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• 2024

Uncertainty of the model, system delay and drive dynamics can be considered as normal uncertainties, and the main source of uncertainty in the seismic control system is related to the nature of the simulated seismic error. In this case, optimizing the management strategy for one particular seismic record will not yield the best results for another. In this article, we propose a framework for online management of active structural management systems with seismic uncertainty. For this purpose, the concept of reinforcement learning is used for online optimization of active crowd management software. The controller consists of a differential controller, an unplanned gain ratio, the gain of which is enhanced using an online reinforcement learning algorithm. In addition, the proposed controller includes a dynamic status forecaster to solve the delay problem. To evaluate the performance of the proposed controllers, thousands of ground motion data sets were processed and grouped according to their spectrum using fuzzy clustering techniques with spatial hazard estimation. Finally, the controller is implemented in a laboratory scale configuration and its operation is simulated on a vibration table using cluster location and some actual seismic data. The test results show that the proposed controller effectively withstands strong seismic interference with delay. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results is believed to achieved in the near future by the ongoing development of AI and control theory.