Influence of Microcrack on Brazilian Tensile Strength of Jurassic Granite in Hapcheon (미세균열이 합천지역 쥬라기 화강암의 압열인장강도에 미치는 영향)
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- Korean Journal of Mineralogy and Petrology
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- v.34 no.1
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- pp.41-56
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- 2021
The characteristics of the six rock cleavages(R1~H2) in Jurassic Hapcheon granite were analyzed using the distribution of ① microcrack lengths(N=230), ② microcrack spacings(N=150) and ③ Brazilian tensile strengths(N=30). The 18 cumulative graphs for these three factors measured in the directions parallel to the six rock cleavages were mutually contrasted. The main results of the analysis are summarized as follows. First, the frequency ratio(%) of Brazilian tensile strength values(kg/㎠) divided into nine class intervals increases in the order of 60~70(3.3) < 140~150(6.7) < 100~110·110~120(10.0) < 90~100(13.3) < 80~90(16.7) < 120~130·130~140(20.0). The distribution curve of strength according to the frequency of each class interval shows a bimodal distribution. Second, the graphs for the length, spacing and tensile strength were arranged in the order of H2 < H1 < G2 < G1 < R2 < R1. Exponent difference(λS-λL, Δλ) between the two graphs for the spacing and length increases in the order of H2(-1.59) < H1(-0.02) < G2(0.25) < G1(0.63) < R2(1.59) < R1(1.96)(2 < 1). From the related chart, the six graphs for the tensile strength move gradually to the left direction with the increase of the above exponent difference. The negative slope(a) of the graphs for the tensile strength, suggesting a degree of uniformity of the texture, increases in the order of H((H1+H2)/2, 0.116) < G((G1+G2)/2, 0.125) < R((R1+R2)/2, 0.191). Third, the order of arrangement between the two graphs for the two directions that make up each rock cleavage(R1·R2(R), G1·G2(G), H1·H2(H)) were compared. The order of arrangement of the two graphs for the length and spacing is reverse order with each other. The two graphs for the spacing and tensile strength is mutually consistent in the order of arrangement. The exponent differences(ΔλL and ΔλS) for the length and spacing increase in the order of rift(R, -0.08) < grain(G, 0.14) < hardway(H, 0.75) and hardway(H, 0.16) < grain(G, 0.23) < rift(R, 0.45), respectively. Fourth, the general chart for the six graphs showing the distribution characteristics of the microcrack lengths, microcrack spacings and Brazilian tensile strengths were made. According to the range of length, the six graphs show orders of G2 < H2 < H1 < R2 < G1 < R1(< 7 mm) and G2 < H1 < H2 < R2 < G1 < R1(≦2.38 mm). The six graphs for the spacing intersect each other by forming a bottleneck near the point corresponding to the cumulative frequency of 12 and the spacing of 0.53 mm. Fifth, the six values of each parameter representing the six rock cleavages were arranged in the order of increasing and decreasing. Among the 8 parameters related to the length, the total length(Lt) and the graph(≦2.38 mm) are mutually congruent in order of arrangement. Among the 7 parameters related to the spacing, the frequency of spacing(N), the mean spacing(Sm) and the graph (≦5 mm) are mutually consistent in order of arrangement. In terms of order of arrangement, the values of the above three parameters for the spacing are consistent with the maximum tensile strengths belonging to group E. As shown in Table 8, the order of arrangement of these parameter values is useful for prior recognition of the six rock cleavages and the three quarrying planes.
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (