• 지질공학
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• 제33권1호
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• pp.1-13
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• 2023

지진은 지체 구조, 지구조 응력, 지각 성분 및 구성 요소 간의 상호 작용을 통해 발생하는 복잡한 현상으로 이해하기 매우 어려운 시스템이기 때문에 예측하기가 쉽지 않다. 우리나라는 평균 M 2.3의 비교적 안전한 지역으로 볼 수 있으나 지진에 대한 대중의 관심이 높아짐에 따라, 한반도의 지진현상을 분석하기 위하여 딥러닝 기반의 Facebook's Prophet 모델을 이용한 시간에 따른 지진패턴의 변화 및 공간과 규모에 따른 지진예측을 시도하였다. 또한, 진앙분포도 군집분석 방법인 DBSCAN과 비교 및 토의하였다. Prophet 지진 예측 모델링 결과 향후 경상북도뿐만 아니라 충청북도, 경기도 및 서울권역에서 지진이 발생할 것으로 예측되었다.

• 한국지진공학회논문집
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• 제10권4호
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• pp.45-54
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• 2006

2005년 3월 20일 발생한 Fukuoka 지진 시 국내 및 일본에서 관측된 자료를 이용하여 지반운동 감쇄특성을 분석하였다. 또한 최근 국내 및 일본에서 제시된 지반운동 거리감쇄식을 이용한 예측값과의 비교를 통하여 국내 강지진동 예측 시 일본에서 개발된 감쇄식의 적용가능성을 평가하였다. 그 결과 동일 거리에 위치한 국내 및 일본 관측소에서 측정한 기록이 비교적 유사하게 나타났으며 국내 및 일본에서 제시된 지반운동 거리감쇄식의 예측값이 매우 유사하게 나타나 국내에서의 강지진동 예측 시 일본에서 제시된 지반운동 감쇄식을 200km 이상의 비교적 원거리에 대한 국내 감쇄식의 평가 등에 유용하게 활용할 수 있는 것으로 나타났다.

• 한국지진공학회논문집
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• 제24권6호
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• pp.277-283
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• 2020

In order to improve the ground-motion prediction equation, which is an important factor in seismic hazard assessment, it is essential to obtain good quality seismic data for a region. The Korean Peninsula has an environment in which it is difficult to obtain strong ground motion data. However, because digital seismic observation networks have become denser since the mid-2000s and moderate earthquake events such as the Odaesan earthquake (Jan. 20, 2007, M_L 4.8), the 9.12 Gyeongju earthquake (Sep. 12, 2016, M_L 5.8), and the Pohang earthquake (Nov. 15, 2017, M_L 5.4) have occurred, some good empirical data on ground motion could have been accumulated. In this study, we tried to build a ground motion database that can be used for the development of the ground motion attenuation equation by collecting seismic data accumulated since the 2000s. The database was constructed in the form of a flat file with RotD50 peak ground acceleration, 5% damped pseudo-spectral acceleration, and meta information related to hypocenter, path, site, and data processing. The seismic data used were the velocity and accelerogram data for events over M_L 3.0 observed between 2003 and 2019 by the Korean National Seismic Network administered by the Korea Meteorological Administration. The final flat file contains 10,795 ground motion data items for 141 events. Although this study focuses mainly on organizing earthquake ground-motion waveforms and their data processing, it is thought that the study will contribute to reducing uncertainty in evaluating seismic hazard in the Korean Peninsula if detailed information about epicenters and stations is supplemented in the future.

• 한국지진공학회논문집
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• 제10권3호
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• pp.65-75
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• 2006

건물의 지진응답을 평가하기 위하여 비선형 푸시오버 해석을 수행한다. 구조물의 비탄성 지진응답을 정확히 예측하기 위해서는, 비선형해석에 사용되는 층하중분포가 구조물의 시간이력 지진응답 동안 실제로 발생되는 지진하중분포를 나타낼 수 있어야 한다. 본 연구에서는 건축물의 지진하중분포를 예측하기 위하여 새로운 모드조합법을 개발하였다. 개발된 모드조합법에서는 모드조합계수를 곱한 각 모드의 스펙트럼응답을 조합하여 다수의 지진하중분포를 예측한다. 모멘트 골조와 켄틸레버 벽체에 대한 변수연구를 수행하였다. 변수연구 결과를 토대로, 각 고유모드가 구조물의 지진응답에 미치는 영향을 나타내는 모드조합계수를 정의하였다. 다양한 정형 및 수직 비정형 구조물에 대하여 제안된 계수모드조합법을 적용하였다. 그 결과 제안된 모드조합법은 시간이력 응답 동안 구조물에 실제로 발생되는 지진하중분포를 정확히 예측할 수 있었다.

• 전자통신동향분석
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• 제35권1호
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• pp.80-88
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• 2020

This paper describes the development trends and service provision examples of disaster occurrence and spread prediction technology for various disasters such as tsunamis, floods, and fires. In terms of fires, we introduce the WIFIRE system, which predicts the spread of large forest fires in the United States, and the Metro21: Smart Cities Institute project, which predicts the risk of building fires. This paper describes the development trends in tsunami prediction technology in the United States and Japan using artificial intelligence (AI) to predict the occurrence and size of tsunamis that cause great damage to coastal cities in Japan, Indonesia, and the United States. In addition, it introduces the NOAA big data platform built for natural disaster prediction, considering that the use of big data is very important for AI-based disaster prediction. In addition, Google's flood forecasting system, domestic and overseas earthquake early warning system development, and service delivery cases will be introduced.

• Structural Engineering and Mechanics
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• 제12권1호
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• pp.51-69
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• 2001

Whereas the potential of static inelastic analysis methods is recognised in earthquake design and assessment, especially in contrast with elastic analysis under scaled forces, they have inherent shortcomings. In this paper, critical issues in the application of inelastic static (pushover) analysis are discussed and their effect on the obtained results appraised. Areas of possible developments that would render the method more applicable to the prediction of dynamic response are explored. New developments towards a fully adaptive pushover method accounting for spread of inelasticity, geometric nonlinearity, full multi-modal, spectral amplification and period elongation, within a framework of fibre modelling of materials, are discussed and preliminary results are given. These developments lead to static analysis results that are closer than ever to inelastic time-history analysis. It is concluded that there is great scope for improvements of this simple and powerful technique that would increase confidence in its employment as the primary tool for seismic analysis in practice.

• 한국지구물리탐사학회:학술대회논문집
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• 한국지구물리탐사학회 2007년도 공동학술대회 논문집
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• pp.81-86
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• 2007

Spectral features of the seismic wave propagation from Odaesan Earthquake were evaluated based on the commonly treated random error between the observed data and the prediction values by the stochastic point-source ground-motion spectral model regarding the source, path and site effects. Radiation pattern of the error according to azimuth angle was found to be similar to the theoretical estimate. It was also observed that the spatial distribution of the errors was correlated with the geological map and the Q0 map which are indicatives of seismic boundaries.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 1998년도 추계 학술발표회 논문집 Proceedings of EESK Conference-Spring 1998
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• pp.369-376
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• 1998

The purpose of the present study is to analyze the response of pipelines subjected to liquefaction-induced permanent ground displacement and to discuss the failure prediction of domestic waterway pipelines. Initially here, characteristics of liquefaction are reviewed and then permanent ground displacement is investigated base on previous earthquake hazard cases. Next, considering the distribution of the transverse permanent ground displacement and equivalent spring constant effect, formulas obtained by a beam theory are established to analyze continuous pipelines. This analysis was performed without consideration of axial effects. So the finite element analysis was used in order to consider the axial stiffness of soil. As a result, degree of liquefaction, width of deformed ground and axial stiffness are crucial points for evaluation the failure of pipelines subjected to permanent ground displacement.

• Structural Engineering and Mechanics
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• 제23권2호
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• pp.177-193
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• 2006

Determining the hysteretic energy demand and dissipation capacity and level of damage of the structure to a predefined earthquake ground motion is a highly non-linear problem and is one of the questions involved in predicting the structure's response for low-performance levels (life safe, near collapse, collapse) in performance-based earthquake resistant design. Neural Network (NN) analysis offers an alternative approach for investigation of non-linear relationships in engineering problems. The results of NN yield a more realistic and accurate prediction. A NN model can help the engineer to predict the seismic performance of the structure and to design the structural elements, even when there is not adequate information at the early stages of the design process. The principal aim of this study is to develop and test multi-layered feedforward NNs trained with the back-propagation algorithm to model the non-linear relationship between the structural and ground motion parameters and the hysteretic energy demand in steel moment resisting frames. The approach adapted in this study was shown to be capable of providing accurate estimates of hysteretic energy demand by using the six design parameters.

• Structural Engineering and Mechanics
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• 제5권6호
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• pp.839-851
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• 1997

Closed-form solutions are analytically derived for stochastic properties of earthquake ground motion fields, which are conditioned by an observed time series at certain observation sites and are characterized by spectra with uncertainties. The theoretical framework presented here can estimate not only the expectations of such simulated earthquake ground motions, but also the prediction errors which offer important information for the field of engineering. Before these derivations are made, the theory of conditional random fields is summarized for convenience in this study. Furthermore, a method for stochastic interpolation of power spectra is explained.