• Steel and Composite Structures
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• v.50 no.5
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• pp.595-608
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• 2024

In this study, aluminum lathe waste was used by replacing aggregates in certain proportions in order to obtain expansive concrete using recycled materials. For this reason, five different aluminum wastes of 1%, 2%, 3%, 4% and 5% were selected and also reference without aluminum waste was produced. Based on the mechanical tests conducted, which included slump, compression, splitting tensile, and flexural tests, it was evident that the workability of the material declined dramatically once the volume ratio of aluminum exceeded 2%. As determined by the compressive strength test (CST), the CS of concrete (1% aluminum lathe wastes replaced with aggregate) was 11% reducer than that of reference concrete. It was noted that the reference concrete's CS values, which did not include aluminum waste, were greater than those of the concrete that contained 5% aluminum. When comparing for splitting tensile strength (STS), it was observed that the results of STS generally follow the parallel inclination as the CS. The reduction in these strengths when 1% aluminum is utilized is less than 10%. These ratios modified 18% when flexural strength (FS) is considered. Therefore, 1% of aluminum waste is recommended to obtain expansive concrete with recycled materials considering minimum loss of strength. Moreover, Scanning Electron Microscope (SEM) analysis was performed and the results also confirm that there was expansion in the aluminum added concrete. The presence of pores throughout the concrete leads to the formation of gaps, resulting in its expansion. Additionally, for practical applications, basic equations were developed to forecast the CS, STS, and FS of the concrete with aluminum lathe waste using the data already available in the literature and the findings of the current study. In conclusion, this study establishes that aluminum lathe wastes are suitable, readily available in significant quantities, locally sourced eco-materials, cost-effective, and might be selected for construction using concrete, striking a balance among financially and ecological considerations.

• Korean Journal of Construction Engineering and Management
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• v.25 no.4
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• pp.24-33
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• 2024

Recently, as smart construction has become more active, construction companys are evaluating their smart construction capabilities in order to transform into smart construction companies. However, the revitalization of smart construction doesn't only apply to construction companies, the level of utilization of all participants, including owners, designers, construction project managers, and construction company, must be improved. Therefore, this study aims to present a model that evaluate the building information modeling (BIM) utilization level for measuring the BIM utilization level of construction management companies in executing construction project management. In this study, an AHP questionnaire survey targeting BIM practitioners to calculate the weight of each BIM utilization item and score it to construct evaluation model and evaluate it by applying it to construction management companies are conducted. As a result of the evaluation using model, there were differences between companies in the number of BIM users, and in the qualitative evaluation, it is mainly used for interference review, constructability review, and design change management. Therefore, in order to revitalize BIM, it is believed that it is necessary to strengthen BIM utilization ability through separate training for construction manager (CMr) and to present clear utilization standards and scope of work for BIM utilization in performing construction management tasks. Consequently, evaluating more construction management companies using the model presented in this study will result in the transition of CM companies to smart construction and revitalization of BIM adoption.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.69-79
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• 2024

Field geotechnical data are obtained from various field and laboratory tests and are documented in geotechnical investigation reports. For efficient design and construction, digitizing these geotechnical parameters is essential. However, current practices involve manual data entry, which is time-consuming, labor-intensive, and prone to errors. Thus, this study proposes an automatic data extraction method from geotechnical investigation reports using image-based deep learning models and text-mining techniques. A deep-learning-based page classification model and a text-searching algorithm were employed to classify geotechnical investigation report pages with 100% accuracy. Computer vision algorithms were utilized to identify valid data regions within report pages, and text analysis was used to match and extract the corresponding geotechnical data. The proposed model was validated using a dataset of 205 geotechnical investigation reports, achieving an average data extraction accuracy of 93.0%. Finally, a user-interface-based program was developed to enhance the practical application of the extraction model. It allowed users to upload PDF files of geotechnical investigation reports, automatically analyze these reports, and extract and edit data. This approach is expected to improve the efficiency and accuracy of digitizing geotechnical investigation reports and building geotechnical databases.

• Journal of Korea Water Resources Association
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• v.57 no.8
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• pp.493-507
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• 2024

RANS-based CFD analysis is widely applied in various engineering fields, including practical hydraulic engineering, due to its high computational efficiency. However, problems of non-physical behavior in the analysis of two phase flow, such as free surfaces, have long been raised. The two-equation turbulence models used in general RANS-based analysis were developed for single phase flow and simulate unrealistically high turbulence energy at the interface where there are abrupt changes in fluid density. To solve this issue, one of the methods recently developed is the buoyancy-modified turbulence model, which has been partially validated in coastal engineering, but has not been applied to open channel flows. In this study, the applicability of the buoyancy-modified turbulence model is evaluated using the VOF method in the open-source program OpenFoam. The results of the uniform flow showed that both the buoyancy-modified k-𝜖 model and the buoyancy-modified k-ω SST model effectively simulated the reduction of turbulence energy near the free surface. Specifically, the buoyancy-modified k-ω SST model accurately simulated the vertical velocity distribution. Additionally, the model is applied to dam-break flows to examine cases with significant surface variation and cavity formation. The simulation results show that the buoyancy-modified turbulence models produce varying results depending on the VOF method and shows non-physical behavior different from experimental results. While the buoyancy-modified turbulence model is applicable in cases with stable surface shapes, it still has limitations in general application when there are rapid changes in the free surface. It is concluded that appropriate adjustments to the turbulence model are necessary for flows with rapid surface changes or cavity formation.

• Journal of Sinology and China Studies
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• v.81
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• pp.41-62
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• 2019

The Wan-Tang(晩唐) period was the time of political and social gloom. This background gave the poets deep thought and sorrow. Therefore, the poets were so emotional that they displayed sensitive and delicate strokes. The poets of this period made metaphorical and symbolic expressions to escape from the gloomy reality and created poems with a sweet mood. They wrapped their sufferings and sorrows in a gentle and beautiful way. In particular, Tingyun,Wen(溫庭筠) and Shangyin,Li(李商隱) were the representative poets of this period. They wrote poems using colorful and soft expressions to escape the dark realities. Their Wan-Yue(婉約) style was also the result of learning the splendor and beauty of the Qi(齐) and Liang(梁) period, meanwhile they were building their own aesthetic art world, which is different from the splendor of the the Qi(齐) and Liang(梁) period. Therefore, this study used poems by Tingyun,Wen(溫庭筠) and Shangyin,Li(李商隱) as analysis targets, and studied 'Wanyue(婉約)' style that appear in Wan-tang(晩唐) period. The 'Wanyue(婉約)' style wasn't just the originality of the 'Ci(詞)'. We found that the time of the cross between poetry and 'Ci(詞)' was formed from a common social and literary background and the creative psychology of poets.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.5
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• pp.30-37
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• 2024

The valuation of infrastructure assets is typically conducted using the straight-line method, which employs the depreciated replacement cost as a basis. However, this approach has the limitation of failing to accurately reflect the actual value of the facility. In light of these considerations, the performance-based depreciation (PBD) method has been proposed as a means of evaluating the asset value of bridges on the basis of their performance, although it is not designed to take account of the environmental characteristics of individual bridges. This study proposes a hazard-performance based depreciation (HPBD) method that considers the risk level of individual bridges in the PBD method proposed in previous studies. The applicability of the proposed method was evaluated on more than 8,000 bridges. The risk factors for deterioration of bridges were selected, the hazard level of individual bridges was evaluated, and weights based on the hazard level were applied to the PBD method. The evaluation resulted in a present value comparable to that of the PBD method and a relatively high future value. It is postulated that the HPBD method, which considers the risk characteristics of individual bridges, can be used for a more reasonable evaluation and decision-making process.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.5
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• pp.62-68
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• 2024

The Republic of Korea experiences four distinct seasons, with significant temperature differences between summer and winter, causing bridges to undergo large temperature variations throughout the year. When the temperature changes, the dynamic characteristics of bridge structures also change. However, during load-bearing capacity assessments in domestic bridge maintenance, this temperature effect is not considered, and only the natural frequency measured over a short period is used for evaluation. In this paper, we theoretically analyze the impact of changes in natural frequency on bridges and extract daily estimated natural frequency data from bridges with continuous vertical acceleration measurements taken over more than a year to confirm temperature-induced changes. The results show that a 1% decrease in natural frequency corresponds to an approximately 2% decrease in the load-bearing capacity of the bridge. Additionally, it was found from the measurement data that a 10℃ increase in temperature did not affect the natural frequency of RC slab bridges and Rahmen bridges, but in PSC-I girder bridges and steel box girder bridges, the natural frequency decreased by approximately 1.04% to 2.48%.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.5
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• pp.421-433
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• 2024

As the utilization of underground space increases, preventing collapse accidents during tunnel construction has become a significant challenge. This study aims to quantitatively assess the risk of tunnel collapse during construction by analyzing various influencing factors and proposing a tunnel collapse risk index based on these factors. For the 14 major influencing factors affecting tunnel collapse, weights were calculated using the analytic hierarchy process (AHP) method. Data from 27 collapse cases were collected, and Monte Carlo simulation was used to calculate the grade scores for each influencing factor. These scores were then synthesized to derive the tunnel collapse risk index. The average value of the tunnel collapse risk index was analyzed to be 49.359 points. Future comparisons with section-by-section evaluation results of tunnel collapse risk will allow for the assessment of whether a specific section has a lower or higher collapse risk. This study provides a systematic method for quantitatively evaluating the key factors of tunnel collapse risk, thereby contributing to the prevention of collapse accidents during tunnel construction and the establishment of appropriate countermeasures. Future research is expected to enhance the reliability of the tunnel collapse risk index by incorporating more field data and improving the accuracy of tunnel collapse risk assessment based on this index.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• Journal of Radiation Protection and Research
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• v.39 no.1
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• pp.7-13
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• 2014

Phosphate rock, phosphogypsum, and products in phosphate processing facility contain naturally occurring radioactive materials (NORM). Therefore, they may give rise to enhanced radiation dose to workers due to inhalation of airborne particulates. Internal dose due to particle inhalation varies depending on particle properties. The objective of the present study was to characterize particle properties at the largest phosphate processing facility in Korea. A cascade impactor was employed to sample airborne particulates at various processing areas in the plant. The collected samples were used for characterization of particle size distribution, particle concentration in the air, and shape analysis. Aerodynamic diameters of airborne particulates ranged 0.03-100 ${\mu}m$ with the highest concentration at the particle size range of 4.7-5.8 ${\mu}m$ (geometric mean = 5.22 ${\mu}m$) or 5.8-9.0 ${\mu}m$ (geometric mean = 7.22 ${\mu}m$). Particle concentrations in the air varied widely by sampling area up to more than two orders of magnitude. The large variation resulted from the variability of mechanical operations and building ventilations. The airborne particulates appeared as spheroids or rough spherical fragments across all sampling areas and sampled size intervals. Average mass densities of phosphate rocks, phosphogypsums, and fertilizers were 3.1-3.4, 2.1-2.6, and 1.7 $gcm^{-3}$, respectively. Radioactivity concentration of uranium series in phosphate rocks varied with country of origin, ranging 94-866 $Bqkg^{-1}$. Among the uranium series, uranium was mostly concentrated on products, including phosphoric acid or fertilizers whereas radium was concentrated on byproducts or phosphogypsum. No significant radioactivity of $^{226}Ra$ and $^{228}Ra$ were found in fertilizer. However, $^{40}K$ concentration in fertilizer was up to 5,000 Bq $g^{-1}$. The database established in this study can be used for the accurate risk assessment of workers due to inhalation of airborne particles containing NORM. In addition, the findings can be used as a basic data for development of safety standard and guide and for practical radiation safety management at the facility.