• Geomechanics and Engineering
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• 제20권2호
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• pp.113-120
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• 2020

Rock damage is the main cause of accidents in underground engineering. It is difficult to predict rock damage accurately by using only one parameter. In this study, a rock failure prediction model was established by using stress, energy, and damage. The prediction level was divided into three levels according to the ratio of the damage threshold stress to the peak stress. A classification predicting model was established, including the stress, energy, damage and AE impact rate using Bayesian method. Results show that the model is good practicability and effectiveness in predicting the degree of rock failure. On the basis of this, a multi-parameter classification predicting deterioration model of rock failure was established. The results provide a new idea for classifying and predicting rockburst.

• 항공우주시스템공학회지
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• 제3권4호
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• pp.27-34
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• 2009

This paper deals with the lifetime prediction of Aircraft Flaperon Joint made of AISI 4130 steel. Reviews are performed on the published damage models at first. And three different damage models are used for predicting the fatigue life of the structure subjected to variable amplitude fatigue loading. These models require no increase in complexity of use, nor do they require additional material property or mission loading information to achieve the improved accuracy. Finally a comparison among the fatigue results is performed. It is observed that the Miner's rule could predict longer life than other cumulative damage models which take into account loads below the endurance limit.

• 통합자연과학논문집
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• 제13권1호
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• pp.34-40
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• 2020

This study aims to create a model for predicting the number of extinguishment manpower to put out forest fires by taking into account the climate, the situation, and the extent of the damage at the time of the forest fires. Past research has been approached to determine the cause of the forest fire or to predict the occurrence of a forest fire. How to deal with forest fires is also a very important part of how to deal with them, so predicting the number of extinguishment manpower is important. Therefore predicting the number of extinguishment manpower that have been put into the forest fire is something that can be presented as a new perspective. This study presents a model for predicting the number of extinguishment manpower inputs considering the scale of the damage with forest fire on a scale bigger than 0.1 ha as data based on the forest fire annual report(Korea Forest Service; KFS) from 2015 to 2018 using the moderated multiple regression analysis. As a result, weather factors and extinguished time considering the damage show that affect forest fire extinguishment manpower.

• 항공우주시스템공학회지
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• 제15권6호
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• pp.10-18
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• 2021

A flame deflector prevents a launch system from thermal damage by deflecting the exhaust flame of the launch vehicle. During the deflection of the flame, the flame deflector is subjected to a high-temperature and high-pressure flow, which results in thermal ablation damage at the surface. Predicting this ablation damage is an essential requirement to ensure a reliable design. This paper introduces a numerical method for predicting the ablation damage phenomena based on a one-way fluid-structure interaction (FSI) analysis. In the proposed procedure, the temperature and convective heat transfer coefficient of the exhaust flame are calculated using a fluid dynamics analysis, and then the ablation is calculated using a finite element analysis (FEA) based on the user-subroutine UMESHMOTION and Arbitrary Lagrangian-Eulerian (ALE) adaptive mesh technique in ABAQUS. The result of such an analysis was verified by comparison to the ablation test result for a flame deflector.

• Nuclear Engineering and Technology
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• 제55권8호
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• pp.2747-2756
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• 2023

Reinforced Concrete (RC) shear walls are one of the civil structures in nuclear power plants to resist lateral loads such as earthquakes and wind loads effectively. Risk-informed and performance-based regulation in the nuclear industry requires considering possible accidents and determining desirable performance on structures. As a result, rather than predicting only the ultimate capacity of structures, the prediction of performances on structures depending on different damage states or various accident scenarios have increasingly needed. This study aims to develop machine-learning models predicting drifts of the RC shear walls according to the damage limit states. The damage limit states are divided into four categories: the onset of cracking, yielding of rebars, crushing of concrete, and structural failure. The data on the drift of shear walls at each damage state are collected from the existing studies, and four regression machine-learning models are used to train the datasets. In addition, the bagging ensemble method is applied to improve the accuracy of the individual machine-learning models. The developed models are to predict the drifts of shear walls consisting of various cross-sections based on designated damage limit states in advance and help to determine the repairing methods according to damage levels to shear walls.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1991년도 봄 학술발표회 논문집
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• pp.61-67
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• 1991

Conventional aseismic design methods of reinforced concrete frame all but disregard the state of damage over the entire building frame. This paper presents an automated damage-contorlled design method, which aims for uniform damage distribution throughout the entire building frame, as measured by the individual mumber damage indexes. Three design parameters, namely the longitudinal steel ratio, the confinement steel ratio and the frame member depth, were studied for their influence on the frame responce to an earthquake. The usefulness of this design method is demonstrated with a four story example office building predicting the extent of structural damage.

• 정보처리학회 논문지
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• 제13권5호
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• pp.217-220
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• 2024

지진 발생은 정확히 예측하기 어렵고, 이러한 무작위성을 갖는 사건에 대비하여 모든 건물에 내진 설계를 도입하는 것은 현실적으로 어려운 과제이다. 건물의 특징 분석을 통한 건물 손상 예측을 기반으로 건물의 취약점을 보완한다면, 내진 설계를 도입하지 않은 건물에서도 피해를 최소화할 수 있으므로 건물 손상 예측 모델의 효율성을 분석하는 연구가 필요하다. 본 논문에서는 2015년 네팔 대지진으로 인해 손상된 건물 데이터를 활용하여 Random Forest, Extreme Gradient Boosting, LightGBM, CatBoost 기계학습 분류 알고리즘을 사용하여 지진 피해 예측 모델의 정확도를 비교하였다.

• 한국환경기술학회지
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• 제18권2호
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• pp.165-172
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• 2017

현 단계에서 자연재해로 인한 피해규모를 정확히 예측하고, 그에 대처하는 것은 어려운 실정이다. 그러나 재해대응 차원에서 피해 규모를 예측할 수 있다면 신속하게 대응하여 피해를 저감할 수 있다고 판단된다. 따라서, 본 연구에서는 여러 가지 자연재해 중 풍랑에 관한 피해액예측함수를 개발하였다. 동해 및 제주 연안지역을 대상으로 국민안전처에서 발간하는 재해연보(1991~2014)의 풍랑 및 태풍피해 이력을 수집하였으며, 물가상승률을 반영하기 위해 2014년 기준으로 피해액을 환산하였다. 또한, 기상청 및 국립해양조사원 홈페이지에서 파고, 풍속, 조위, 파향, 파주기 등의 기상 자료를 수집하였다. 풍랑피해가 발생했을 때 연안지역의 특성을 반영하기 위해 2015년 국립해양조사연구원에서 발행한 연안재해노출지수(Coastal Disaster Index; CODI), 연안민감도지수(Coastal Sensitivity Index; COSI), 연안재해영향지수(Coastal Potential Impact Index; CPII)를 반영하였다. 피해 발생 시 기상현황, 지역특성을 나타내는 지수, 피해액과의 상관관계를 통해 풍랑피해액예측함수를 개발하였다.

• Wind and Structures
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• 제15권5호
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• pp.369-383
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• 2012

Researchers have recently begun using high spatial resolution remote-sensing data, which are automatically captured and georeferenced, to assess damage following natural and man-made disasters, in addition to, or instead of employing the older methods of walking house-to-house for surveys, or photographing individual buildings from an airplane. This research establishes quantitative relationships between the damage states observed at ground-level, and those observed from space using high spatial resolution remote-sensing data, for windstorms, for individual site-built one- or two-family residences (FR12). "Degrees of Damage" (DOD) from the Enhanced Fujita (EF) Scale were determined for ground-based damage states; damage states were also assigned for remote-sensing imagery, using a modified version of Womble's Remote-Sensing (RS) Damage Scale. The preliminary developed model can be used to predict the ground-level damage state using remote-sensing imagery, which could significantly lessen the time and expense required to assess the damage following a windstorm.

• Steel and Composite Structures
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• 제49권1호
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• pp.65-79
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• 2023

This paper presents a development of empirical evaluations, which can be used to evaluate the damage of fiber-reinforced concrete composites (FRC) wall subjected to close-in blast loads. For this development, a combined application of numerical simulation and machine learning approaches are employed. First, finite element modeling of FRC wall under blast loading is developed and verified using experimental data. Numerical analyses are then carried out to investigate the dynamic behavior of the FRC wall under blast loading. In addition, a data set of 384 samples on the damage of FRC wall due to blast loads is then produced in order to develop machine learning models. Second, three robust machine learning models of Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) are employed to propose empirical evaluations for predicting the damage of FRC wall. The proposed empirical evaluations are very useful for practical evaluation and design of FRC wall subjected to blast loads.