A Study on Chloride Binding Capacity of Various Blended Concretes at Early Age (초기재령에서 각종 혼합콘크리트의 염소이온 고정화능력에 관한 연구)
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- Journal of the Korea institute for structural maintenance and inspection
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- v.12 no.5
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- pp.133-142
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- 2008
This paper studies the early-aged chloride binding capacity of various blended concretes including OPC(ordinary Portland cement), PFA(pulversied fly ash), GGBFS(ground granulated blast furnace slag) and SF(silica fume) cement paste. Cement pastes with 0.4 of a free water/binder ratio were cast with chloride admixed in mixing water, which ranged from 0.1 to 3.0% by weight of cement and different replacement ratios for the PFA, GGBFS and SF were used. The content of chloride in each paste was measured using water extraction method after 7 days curing. It was found that the chloride binding capacity strongly depends on binder type, replacement ratio and total chloride content. An increase in total chloride results in a decrease in the chloride binding, because of the restriction of the binding capacity of cement matrix. For the pastes containing maximum level of PFA(30%) and GGBFS(60%) replacement in this study, the chloride binding capacity was lower than those of OPC paste, and an increase in SF resulted in decreased chloride binding, which are ascribed to a latent hydration of pozzolanic materials and a fall in the pH of the pore solution, respectively. The chloride binding capacity at 7 days shows that the order of the resistance to chloride-induced corrosion is 30%PFA > 10%SF > 60%GGBFS > OPC, when chlorides are internally intruded in concrete. In addition, it is found that the binding behaviour of all binders are well described by both the Langmuir and Freundlich isotherms.
When cavitation noise occurs in ship propellers, the level of underwater radiated noise abruptly increases, which can be a critical threat factor as it increases the probability of detection, particularly in the case of naval vessels. Therefore, accurately and promptly assessing cavitation signals is crucial for improving the survivability of submarines. Traditionally, techniques for determining cavitation occurrence have mainly relied on assessing acoustic/vibration levels measured by sensors above a certain threshold, or using the Detection of Envelop Modulation On Noise (DEMON) method. However, technologies related to this rely on a physical understanding of cavitation phenomena and subjective criteria based on user experience, involving multiple procedures, thus necessitating the development of techniques for early automatic recognition of cavitation signals. In this paper, we propose an algorithm that automatically detects cavitation occurrence based on simple statistical features reflecting cavitation characteristics extracted from acoustic signals measured by sensors attached to the hull. The performance of the proposed technique is evaluated depending on the number of sensors and model test conditions. It was confirmed that by sufficiently training the characteristics of cavitation reflected in signals measured by a single sensor, the occurrence of cavitation signals can be determined.
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 (