• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2002.11a
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• pp.307-313
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• 2002

Urban style row-rise houses for rent have been supplied to the center of city from the 1950's first half The amount of stock is about 450,000 houses and occupies about 40％ in the private rented house. As for the structure, the 60％ of them is wooden-structure and the rest is non-wooden. Stocks of 57.5％ of the wooden-structure and 86％ of the non-wooden were built after 1981 years. It was 1981 years that the new earthquake-resistant standard was carried out, the improvement of stocks built before 1981 is necessary because those don't satisfy the present standard. To investigate the renovation possibility of urban style row-rise houses for rent, actual situation of two-story apartment at 2,4, and 5 Chome Taito-Ku in Tokyo was surveyed from July to November 2001. The number of building analyzed is 227 ridges among 234 ridges of the surveyed two-story building. 90％ of building analyzed is wooden structure. 1) The site of 88％ building surveyed is close to a road less than 4m width. It becomes the existing non-conformed building in the building construction act. It is impossible to make a renovation such as rebuilding, extension and remodeling because it requires the set back when renovating this type of non-conformed building, 2) The building built before 1981 is almost wooden-structure, and occupies 37％ of the building surveyed, and doesn't satisfy the present earthquake-resistant standard. An improvement of them is needed because the decrepitude of building proceeds. 3) 50％ of openings and 40％ of windows of the building surveyed doesn't use noncombustible materials. Though it becomes the existing non-conformed building, it can satisfy the present standard by replacing openings with noncombustible materials.

• Journal of the Korean Solar Energy Society
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• v.40 no.5
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• pp.47-58
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• 2020

Ministry of Land, Infrastructure and Transport(MOLIT) has increased reduction rate from 18.1% to 32.7% in Building sector compared to BAU of the national greenhouse gas emission according to the 2030 Greenhouse Gas Reduction Road map Amendment. For this purpose, MOLIT has been activating the green remodeling projects for existing buildings. Considering that 15 year old buildings after completion are 74% (5.25 million buildings) among about 7 million existing building stocks in Korea, reduction of building energy consumption by green remodeling is urgently needed, However, it is a major difficulty of activation for green remodeling projects because there are few case studies on Before and After building energy consumption of actual green remodeling projects. Considering that building energy performance and value increase after green remodeling through previous researches, additional studies of the energy consumption assessment on actual green remodeling projects are essential. Therefore, this study aims to propose results on Before and After building energy consumption of actual green remodeling projects.

• Earthquakes and Structures
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• v.9 no.4
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• pp.893-910
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• 2015

Earthquake safety of existing buildings has gained considerable importance after earthquakes which have occurred in our country especially in the last 30 years. Performance based assessment methods have been widely used for existing reinforced concrete structures. This study aims to investigate the earthquake performances of the building stock located in Van Lake basin in Eastern Anatolia of Turkey. The case study of buildings has been modeled on and the structural performances have been determined by employing the non-linear methods described in the latest Turkish Earthquake Code published in 2007. The Van lake basin is located on the very seismically active in a region. On October 23, 2011, a magnitude of Mw 7.2 earthquake struck the Van province in eastern Turkey. The earthquake ground motion was recorded as about 0.1g in Bitlis province. Performance evaluations have been performed by taking samples from each district consisting urban building stocks of Bitlis. A total of 16 reinforced concrete buildings have been evaluated. Among them, 53% of those buildings were determined in the Fully Operational performance level; 13% of them in the Life Safety performance and 34% of them could not be evaluated because of the ratio of the effective mass of first mode to the total mass of the buildings was smaller than 0.70. Therefore, incremental equivalent seismic load methods, which are a part of Turkish Earthquake Code -2007, cannot be used.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.32-39
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• 2009

"We need to overcome the lack of space and infrastructure." Construction in the 20th century had been basically motivated by this need. It had enabled almost automatic financing on land as well as plenty of public investment. However we cannot expect such investment in built environment any more in the 21st century, when urban structures have been fully built. "We have space and infrastructures but want to make them better." We must find the new way how to strategically organize creativity on use as well as such users' want itself for sustaining investment in built environment. In this paper the outline of such a new way is discussed.

• Advances in Computational Design
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• v.4 no.2
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• pp.119-139
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• 2019

Planning and management building projects should tackle the coordination of works and the management of limited spaces, traffic and supplies. Activities cannot be performed without the resources available and resources cannot be used beyond the capacity of workplaces. Otherwise, workspace congestion will negatively affect the flow of works. Better on-site management allows for substantial productivity improvements and cost savings. The procurement system should be able to manage a wider variety of materials and products of the required quality in order to have less stock, in less time, using less space, with less investment and avoiding multiple storage stations. The objective of this paper is to demonstrate the advantages of using the Chronographic modeling, by combining spatiotemporal technical scheduling with the 4D simulations, the Last Planner System and the Takt-time when modeling the construction of building projects. This paper work toward the aforementioned goal by examining the impact that material flow has on site occupancy. The proposed spatiotemporal model promotes efficient site use, defines optimal site-occupancy and workforce-rotation rates, minimizes intermediate stocks, and ensures a suitable procurement process. This paper study the material flow on the site and consider horizontal and vertical paths, traffic flows and appropriate means of transportation to ensure fluidity and safety. This paper contributes to the existing body of knowledge by linking execution and supply to the spatial and temporal aspects. The methodology compare the performance and procurement processes for the proposed Chronographic model with the Gantt-Precedence diagram. Two examples are presented to demonstrate the benefits of the proposed model and to validate the related concepts. This validation is designed to test the model's graphical ability to simulate construction and procurement.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.105-111
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• 2023

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.