• Journal of the Korean Geotechnical Society
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• v.28 no.3
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• pp.35-43
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• 2012

Following the companion paper (I. Comparisons with Well-known Seismic Code and Site Response Characteristics), several acceleration data recorded during recent earthquake events in Korea were analyzed to verify the suitability of the proposed two-parameters site classification system and the corresponding site coefficients. For all of rock-soil site pairs less than 30 km distant, response spectrums and corresponding site coefficients, $F_a$ and $F_v$, were determined. Unfortunately, some of data have an eccentric error, where the spectral acceleration of rock site is more amplified than that of soil site. The $F_a$ and $F_v$ for all of pairs except the pairs of error were compared with those in the current code and the proposed system. The $F_a$ and $F_v$ from the recorded motions show definitely different trend from that of the current code. In addition, the site coefficients from recorded motions at four 765 kV substation sites, which are several hundred meters distant, have a remarkably similar trend and absolute values to those in proposed two-parameters site classification system. Based on earthquake motions recorded in domestic areas including data from the four 765 kV substation sites, the two-parameters site classification and site coefficients are superior to the results obtained from the current Korean seismic code.

• Journal of the Korean Geotechnical Society
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• v.28 no.3
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• pp.25-34
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• 2012

In order to verify that the recently proposed two-parameters site classification system and the corresponding site coefficients are suitable for the local geological conditions in Korea, a comparison was conducted with current Korean seismic code, Eurocode-8, NYC DOT seismic code. The design spectrum of the current Korean seismic code is significantly amplified in the long-period range, whereas the other response spectra, including the proposed two-parameters approach, are significantly amplified in the short-period range, which is a typical geological condition in Korea. In addition, based on the results of site response analyses in the specific $10km{\times}10km$ area of Gyeongju, spatial distributions of site coefficients from site-specific seismic response analyses were compared with the proposed site coefficients, as well as those specified in the current Korean seismic code. The site coefficients ($F_a$ and $F_v$) from the current Korean seismic codes show significantly high spatial error distributions compared with those specified by the two-parameters site classification system. Therefore, the proposed system is suitable for regions of shallow bedrock including the Korean peninsula.

• Journal of the Earthquake Engineering Society of Korea
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• v.20 no.4
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• pp.245-256
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• 2016

In the companion paper (I - Database and Site Response Analyses), site-specific response analyses were performed at more than 300 domestic sites. In this study, a new site classification system and design response spectra are proposed using results of the site-specific response analyses. Depth to bedrock (H) and average shear wave velocity of soil above the bedrock ($V_{S,Soil}$) were adopted as parameters to classify the sites into sub-categories because these two factors mostly affect site amplification, especially for shallow bedrock region. The 20 m of depth to bedrock was selected as the initial parameter for site classification based on the trend of site coefficients obtained from the site-specific response analyses. The sites having less than 20 m of depth to bedrock (H1 sites) are sub-divided into two site classes using 260 m/s of $V_{S,Soil}$ while the sites having greater than 20 m of depth to bedrock (H2 sites) are sub-divided into two site classes at $V_{S,Soil}$ equal to 180 m/s. The integration interval of 0.4 ~ 1.5 sec period range was adopted to calculate the long-period site coefficients ($F_v$) for reflecting the amplification characteristics of Korean geological condition. In addition, the frequency distribution of depth to bedrock reported for Korean sites was also considered in calculating the site coefficients for H2 sites to incorporate sites having greater than 30 m of depth to bedrock. The relationships between the site coefficients and rock shaking intensity were proposed and then subsequently compared with the site coefficients of similar site classes suggested in other codes.

• The Journal of Engineering Geology
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• v.24 no.2
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• pp.247-260
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• 2014

Rock masses at the site for the $2^{nd}$ step construction of the KAERI Underground Research Tunnel (KURT) are divided into six units to establish a rock mechanics model that is dependent on the geological characteristics and degree of joint development. The site primarily consists of three granitic units (G1, G2, and G3), two dykes (D1 and D3), and a fault zone of poor rock mass quality (F3). The F3 unit crosses the tunnel at the beginning of the site of $2^{nd}$ step construction. The rock masses of each unit are classified by RMR (Rock Mass Rating), Q-system, and RMi (Rock Mass Index), all based on borehole logging data. The deformation modulus, rock mass strength, cohesion, and friction angle for each unit are calculated using established empirical relationships. The representative rock mass classification and geotechnical parameters for the rock mass units are established, and a rock mechanics model for the site is proposed, which will be useful in the design and stability analysis of the $2^{nd}$ step construction of KURT.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.