• Journal of the Earthquake Engineering Society of Korea
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• v.15 no.6
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• pp.19-31
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• 2011

The current Korean criteria for seismic performance evaluated by dynamic analysis regulates that the horizontal displacement and vertical settlement of a dam body, including the static deformation, should be within 1% of the dam height. However, there has been weak theoretical support, so that the current criteria have to be validated. Korea is in a region of low or moderate seismicity located inside the Eurasian plate, and few earthquakes with considerable magnitudes and intensities have been recorded in the area. Therefore, in this study, published data measured in overseas countries were collected in order to construct a database and validate the current criteria. In addition, dynamic centrifuge tests and a parametric study using numerical simulations were performed in order to investigate the effect on the horizontal displacement and settlement of a dam body and to validate the current criteria.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.23-29
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• 2019

In this paper, a deep learning-based detection and classification using multi-band frequency signals is presented for detecting earthquakes prevalent in Korea. Based on an analysis of the previous earthquakes in Korea, it is observed that multi-band signals are appropriate for classifying earthquake signals. Therefore, in this paper, we propose a deep CNN (Convolutional Neural Network) using multi-band signals as training data. The proposed algorithm extracts the multi-band signals (Low/Medium/High frequency) by applying band pass filters to mel-spectrum of earthquake signals. Then, we construct three CNN architecture pipelines for extracting features and classifying the earthquake signals by a late fusion of the three CNNs. We validate effectiveness of the proposed method by performing various experiments for classifying the domestic earthquake signals detected in 2018.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.377-388
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• 2022

Recently, several studies have utilized machine learning to efficiently and accurately analyze seismic data that are exponentially increasing. In this study, we expand earthquake information such as occurrence time, hypocentral location, and magnitude to produce a dataset for applying to machine learning, reducing the dimension of the expended data into dominant features through principal component analysis. The dimensional extended data comprises statistics of the earthquake information from the Global Centroid Moment Tensor catalog containing 36,699 seismic events. We perform data preprocessing using standard and max-min scaling and extract dominant features with principal components analysis from the scaled dataset. The scaling methods significantly reduced the deviation of feature values caused by different units. Among them, the standard scaling method transforms the median of each feature with a smaller deviation than other scaling methods. The six principal components extracted from the non-scaled dataset explain 99% of the original data. The sixteen principal components from the datasets, which are applied with standardization or max-min scaling, reconstruct 98% of the original datasets. These results indicate that more principal components are needed to preserve original data information with even distributed feature values. We propose a data processing method for efficient and accurate machine learning model to analyze the relationship between seismic data and seismic behavior.

• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.217-220
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• 2024

Predicting earthquake occurrences accurately is challenging, and preparing all buildings with seismic design for such random events is a difficult task. Analyzing building features to predict potential damage and reinforcing vulnerabilities based on this analysis can minimize damages even in buildings without seismic design. Therefore, research analyzing the efficiency of building damage prediction models is essential. In this paper, we compare the accuracy of earthquake damage prediction models using machine learning classification algorithms, including Random Forest, Extreme Gradient Boosting, LightGBM, and CatBoost, utilizing data from buildings damaged during the 2015 Nepal earthquake.

• Economic and Environmental Geology
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• v.52 no.6
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• pp.573-586
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• 2019

A seismic hazard map based on spatial analysis of various sources of geologic seismic information was developed and assessed for regional seismic vulnerability in South Korea. The indicators for assessment were selected in consideration of the geological characteristics affecting the seismic damage. Probabilistic seismic hazard and fault information were used to be associated with the seismic activity hazard and bedrock depth related with the seismic damage hazard was also included. Each indicator was constructed of spatial information using GIS and geostatistical techniques such as ordinary kriging, line density mapping and simple kriging with local varying means. Three spatial information constructed were integrated by assigning weights according to the research purpose, data resolution and accuracy. In the case of probabilistic seismic hazard and fault line density, since the data uncertainty was relatively high, only the trend was intended to be reflected firstly. Finally, the seismic activity hazard was calculated and then integrated with the bedrock depth distribution as seismic damage hazard indicator. As a result, a seismic hazard map was proposed based on the analysis of three spatial data and the southeast and northwest regions of South Korea were assessed as having high seismic hazard. The results of this study are expected to be used as basic data for constructing seismic risk management systems to minimize earthquake disasters.

• Geophysics and Geophysical Exploration
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• v.12 no.4
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• pp.316-327
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• 2009

A standard for national seismological observatory was proposed on 1999. Since then, Quanterra digitizer has been installed and is operating on almost all of seismic stations which belong to major seismic monitoring organizations. Quanterra digitizer produce and transmit real-time event packet and data packet. Characteristics of event packet and arrival time of each channel's data packet on data center were investigated. Packet selection criteria using signal to noise ratio (hereafter SNR) and signal period from real-time event packet based on 100 samples per second (hereafter sps) velocity data were developed. Estimation of epicenter using time information of the selected event packet were performed and tested. A series of experiment show that event packets were received approximately 3~4 second earlier than data packets and the number of event packet was only 0.3% compare to data packets. Just about 5% against all of event packets were selected as event packet were related P wave of real earthquake. Using the selected event packets we can estimate an epicenter with misfit less than 10 km within 20 sec for local earthquake over magnitude 2.5.

• Journal of the Korean earth science society
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• v.42 no.1
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• pp.76-87
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• 2021

In this study, temporal trend of researches in earthquake with groundwater level, water quality, radon, remote sensing, electrical resistivity, gravity, and geomagnetism was searched from 2001 to 2020, using the journals indexed in Web of Science, and the number of articles published in international journals was counted in relation to the occurrences of earthquakes (≥Mw 5.0, ≥Mw 6.0, ≥Mw 7.0, ≥Mw 8.0, and ≥Mw 9.0). The number of articles shows an increasing trend over the studied period. This is explained by that studies on earthquake precursor and seismic monitoring becomes active in various fields with integrated data analysis through the development of remote sensing technology, progress of measurement equipment, and big data. According to Mann-Kendall and Sen's tests, gravity-related articles exhibit an increasing trend of 1.30 articles/yr, radon-related articles (0.60 articles/yr), groundwater-related articles (0.70 articles/yr), electrical resistivity-related articles (0.25 articles/yr), and remote-sensing-related articles (0.67 articles/yr). By cross-correlation analysis of the number of articles in each field with removing trend effect and the number of earthquakes of ≥Mw 5.0, ≥Mw 6.0, ≥Mw 7.0, ≥Mw 8.0, and ≥Mw 9.0, radon and remote sensing fields exhibit a high cross-correlation with a delay time of one year. In addition, large-scale earthquakes such as the 2004 and 2005 Sumatra earthquake, the 2008 Sichuan earthquake, the 2010 Haiti earthquake, and the 2010 Chile earthquake are estimated to be related with the increase in the number of articles in the corresponding periods.

• Journal of the Korean Geotechnical Society
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• v.20 no.7
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• pp.15-24
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• 2004

This paper describes a GIS-based assessment of residential building damage caused by the 1994 Northridge earthquake in which the fractions of existing buildings damaged at various percentages of replacement cost are related to a range of seismic parameters. The assessment uses data from safety inspection reports and tax assessor records, both of which were geocoded and linked to seismic parameters derived from strong motion records at 164 different sites. The paper also describes a GIS-based pattern recognition algorithm for identifying locations of most intense building damage. The algorithm provides a framework for rapidly screening remote sensing data and dispatching emerging services.

• Annual Conference of KIPS
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• 2005.05a
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• pp.1653-1656
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• 2005

볼륨 렌더링은 스칼라 데이터로 구성된 3 차원 볼륨 데이터를 가시화하는 기법을 가리키며, 유체 역학, 지진, 기상, 해안, 천문, 의료 등 다양한 분야에서 데이터를 분석하는데 널리 사용된다. 최근에는 대용량 볼륨 데이터가 생성되면서 고해상도 디스플레이에 대한 요구가 높아졌으며, 이에 따라 타일형 디스플레이 장치에서 볼륨 데이터를 가시화하려는 시도가 많이 이뤄지고 있다. 본 논문에서는 타일형 디스플레이 장치에서 볼륨 데이터를 가시화하는 기법을 구현했다. 볼륨 데이터 렌더링은 타일형 디스플레이 장치와 연결된 PC-클러스터에서 그래픽스 하드웨어를 사용하는 볼륨 렌더링 기법으로 수행했으며, 이렇게 렌더링된 결과 이미지를 컴포지팅함으로써 해당 디스플레이 장치에 적절한 이미지를 생성했다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.287-295
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• 2023

The collapse of bridges due to earthquakes results in many casualties and property damages. Thus, accurate prediction and preparation are required for the behavior of bridges during earthquakes. In particular, columns play an important role in the seismic behavior of bridges. The risk of collapse due to an earthquake increases when there is a problem of the insufficient lap splice in the column. In this study, to analyze the characteristics of the lap splice in the column, a numerical model was defined for the insufficient lap-spliced columns and verified using experimental data. The developed column model was applied to a commonly used RC slab bridge. Nonlinear static analysis for the column was performed to evaluate the change in the performance of the column according to the lap-spliced length. In addition, this study assessed the effect of the lap-spliced length on the seismic fragility analysis.