• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.4
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• pp.233-252
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• 2021

This paper is a study to improve the efficiency of mixing technology in the shield TBM chamber. Currently, the number of construction cases using the TBM method is increasing in Korea. According to the increasing use of TBM method, research on TBM method such as Disc Cutter, Cutter bit, and Segment also shows an increasing trend. However, there is little research on the mixing efficiency in chamber and chamber. In order to improve the smooth soil treatment and the behavior of the excavated soil, a study was conducted on the change of the mixing efficiency according to the effective mixing bar arrangement in the chamber. In the scale model experiment, the ground was composed using plastic materials of different colors for ease of identification. In addition, the mixing bar arrangement was different and classified into 4 cases, and the particle size distribution was classified into single particle size and multiple particle size, and the experiment was conducted with a total of 8 cases. The rotation speed of the cutter head of all cases was the same as 5 RPM, and the experiment time was also carried out in the same condition, 1 minute and 30 seconds. In order to check the mixing efficiency, samples at the upper, middle (left or right), and lower positions of each case were collected and analyzed. As a result of the scaled-down model experiment, the mixing efficiency of Case 4 and Case 4-1 increased compared to Case 1 and Case 1-1, which are actually used. Accordingly, it is expected that the mixing efficiency can be increased by changing the arrangement of the mixing bar in the chamber, and it is considered to be effective in saving air as the mixing efficiency increases. Therefore, this study is considered to be an important indicator for the use of shield TBM in Korea.

• Economic and Environmental Geology
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• v.54 no.2
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• pp.233-245
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• 2021

Many studies on the microstructures in rocks have been conducted using experimental methods with various equipment as well as natural rock studies to see the development of microstructures and understand their mechanisms. Grain boundary migration of mineral aggregates in rocks could cause grain growth or grain size changes during metamorphism or deformation as one of the main recrystallization mechanisms. This study suggests improved ways regarding the analog material experiments with reformed equipment to see sequential observations of these grain boundary migration. It can be more efficient than the existing techniques and carry out an appropriate microstructure analysis. This reformed equipment was implemented to enable optical manipulation by mounting polarizing plates capable of rotating operation on a stereoscopic microscope and a deformation rig capable of experimenting with analog materials. The equipment can automatically control the temperature and strain rate of the deformation rig by microcontrollers and programming and can take digital photomicrographs with constant time intervals during the experiment to observe any microstructure changes. The composite images synthesized using images by rotated polarizing plates enable us to see more accurate grain boundaries. As a rock analog material, norcamphor(C7H10O) was used, which has similar birefringence to quartz. Static grain growth and simple shear deformation experiments were performed using the norcamphor to verify the effectiveness of the equipment. The static grain growth experiments showed the characteristics of typical grain growth behavior. The number of grains decreases and the average grain size increases over time. These case experiments also showed a clear difference between the growth curves with three temperature conditions. The result of the simple shear deformation experiment under the medium temperature-low strain rate showed no significant change in the average grain size but presented the increased elongation of grain shapes in the direction of about 53° regarding the direction perpendicular to the shearing direction as the shear strain increases over time. These microstructures are interpreted as both the plastic deformation and the internal recovery process in grains are balanced by the deformation under the given experimental conditions. These experiments using the reformed equipment represent the ability to sequentially observe changing the microstructure during experiments as desired in the tests with the analog material during the entire process.

• Journal of the Korean GEO-environmental Society
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• v.22 no.6
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• pp.17-26
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• 2021

Ground investigation and result analysis generally used to examine all sorts of structures' subsidence or stability can be classified into sandy soil and cohesive soil, and analysis on the liquid limit of soil is utilized to evaluate the physical properties of ground and types or technical behavior of soil. The most widely used method to analyze liquid limit is Casagrande with which liquid limit can be calculated relatively easily; however, it is fairly difficult to apply it to soil equipped with intermediate properties. Therefore, concerning the properties of soil having the intermediate properties of sedimentary ground, this researcher mixed the clay from Yangsan, Gwangyang, and Busan with sandy soil to make intermediate soil and then carried out the test of consistency limit and also evaluated applicability by using the suggested formula of consistency revision. The sample of intermediate soil was the mixture of clay and sandy soil, and to produce intermediate soil, the content (F_c) of fine soil was applied as 50%, 75%, or 100%. Regarding the physical properties of intermediate soil, to maintain the properties of clay in the natural state, bentonite was added at a fixed rate for controlling the properties of clay, and then, consistency was analyzed. By adopting the formula of consistency revision suggested in advanced research, this author analyzed consistency based on the experiment and consistency based on the suggested formula of revision. Also, about intermediate soil collected at the site, consistency based on the experiment and consistency based on the suggested formula of revision were analyzed comparatively, and about intermediate soil collected, this researcher analyzed particle size and calculated the content (F_c) of fine soil to analyze intermediate soil in diverse conditions. Moreover, about intermediate soil collected at the site, the suggested formula of consistency revision was applied to calculate the compression index, and the compression index based on the experiment and the compression index based on the suggested formula were analyzed comparatively to evaluate the applicability of the suggested formula.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.