• Land and Housing Review
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• v.3 no.4
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• pp.423-431
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• 2012

The trends of Green growth and environmentally-friendliness came to be the key development indicator for sustaining global environment for the future. Korean government reflected these trends in the main flows of the national development index. Korean construction industry is concentrating investment on fields related to these trends such as technologies for carbon dioxide deduction, materials or products for environmentally-friendly building and alternative energy development, as well. However, efforts to follow these trends during the construction process as a step for production phrase are not being considered enough. In this study, we analysed current status and points to be improved of on-site environment management by surveying major project owners and representatives of the environmental affairs in the leading companies of those fields. Also, we suggested organizational structure and operation model as a solution for enhancing environmentally-friendliness for systematic approach to manage on-site environment in public construction sites. In addition to these solutions, further approaches such as shifting paradigm of project owners and contractors, securing workforce and practical budget for efficient management activities, developing environmental management manuals, setting standards for evaluation of activities are needed for the stable settlement of on-site environmental management.

• Land and Housing Review
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• v.12 no.2
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• pp.1-12
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• 2021

The purpose of this study is to provide implications for the demonstration and the spread of smart cities. For this, the European Lighthouse project, a well-known smart city initiative, is analyzed. This article investigates agendas, applied solutions, stakeholders and cooperation, and expansion strategies in the 18 Lighthouse projects implemented by the EIP-SCC Smart City Demonstration Project. Four implications are discussed for Korean smart city projects. First, in promoting smart cities, clear philosophies and principles should be established in response to global issues such as climate change. Second, smart cities should be understood as the approaches for problem-solving rather than simply applying advanced technology. Third, a business system should be established in a way to meet the needs of the actors participating in the smart city demonstration project. Finally, an evaluation framework should be established to verify the performance of the project. High-performing best-practice projects should be supported by articulating their standards for their lessons to be spread into other cities. The implications presented here can contribute to developing smart city models along with follow-up studies.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1994.04a
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• pp.94-99
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• 1994

최근 인구의 과밀화와 이에 따른 지가의 상승 등으로 대표적인 주거용 빌딩인 아파트의 고층화가 급속하게 진행되고 있고 아파트에 설치되는 엘리베이터도 점차 대형화, 고속화 되고 있다. 실례로 15층 아파트의 경우 11인승, 60m/min의 엘리베이터가 일반적으로 사용되는 반면, 20층 아파트의 경우 17인승, 90m/min이 주로 사용된다. 이와 같이 엘리베이터의 용량과 운행 속도가 증가함에 따라 필연적으로 소음 및 진동 문제가 발생하게 된다. 특히 아파트의 경우 침실, 공부방 등 고도의 정숙을 요하는 생활 공간이 많고, 내부 공간의 활용도를 높이기 위하여 이들 방들이 엘리베이터 기계실 또는 운행 통로와 직접 접하여 있는 경우도 있어 이 경우 소음, 진동 문제는 아주 심각한 문제로 대두된다. 본 연구소가 측정한 방에 의하면 S신도시 L아파트의 경우 아파트 최상층 방에서 실내 소음도가 46dB(A), 벽의 진동가속도가 3.4mm/s$^{2}$(RMS)으로 나타났다. 진동의 경우 생활에 직접적인 악영향을 미칠 수준은 아니지만 소음의 경우 ASHRAE 권장 주택소음 기준치가 35dB(A) 이하임ㅇㄹ 감안하면 주거에 곤란한 수준이다. 수년전, 고층 아파트가 보급될 초기에는 아파트에 엘리베이터가 설치되어있다는 그 자체만으로 충분한 장점이 되어 다소음 소음, 진동문제는 큰 불만거리가 되지 않았지만 엘리베이터가 보편화된 지금에는 엘리베이터의 편리성만으로는 점점더 크게 요구되는 정숙성이 보상되지 않는다. 따라서 전반적인 아파트의 소음, 진동 문제에 큰 비중을 차지하는 엘리베이터에 의한 소음, 진동에 관하여 체계적인 연구가 필요하다. 이에 본고에서는 엘리베이터에 의한 아파트의 소음 및 진동에 관하여 그 현황, 원인 그리고 대책에 관한여 논하고자 한다.감 방법을 연구하였고, T.Sakai는 5자유도 모델을 이용하여 엔진 공회전시 발생하는 치타음에 대해 이론과 실험을 통해 해석하고, 엔진 회전수 변동, 클러치 특성, 변속기의 드래그(drag) 토크의 영향과 치타음 저감을 위한 개선된 클러치 특성을 제시하였다. 이 외에도 Thomas C.T.와 E.P.Petkus는 특정 차량에 대한 동력전달계의 비틀림 진동 현상에 대해 연구하였다. 이러한 연구들로 볼 때, 자동차 동력전달계에서 발생하는 진동은 이론과 실험을 통해 그 해석이 가능하며 설계에 매우 유용하게 이용되고 있음을 알 수 있다. 따라서, 본 연구는 4 실린더 4 싸이클 1.5 L 엔진을 장착한 경승용차의 실차 주행실험을 통해 가속 페달의 급조작에 따른 차체의 종진동 현상을 측정하고, 엔진-변속기-타이어-차체의 반환정계 4자유도 진동모델로 시뮬레이션을 수행하여 실차 주행실험의 결과치와 비교, 분석한 후 클러치 비틀림 특성을 비롯한 자동차 동력전달계의 각 설계인자들이 차체의 종진동에 어떠한 영향을 미치는가를 해석하고자 한다.be presented.LIFO, 우선 순위 방식등을 선택할 수 있도록 확장하였다. SIMPLE는 자료구조 및 프로그램이 공개되어 있으므로 프로그래머가 원하는 기능을 쉽게 추가할 수 있는 장점도 있다. 아울러 SMPLE에서 새로이 추가된 자료구조와 함수 및 설비제어 방식등을 활용하여 실제 중형급 시스템에 대한 시뮬레이션 구현과 시스템 분석의 예를 보인다._3$", chain segment, with the activation energy of carriers from the shallow trap with 0.4[eV], in he amorphous regions.의 증발산율은 우기의 기상자료를 이용하여 구한 결과 0.05 - 0.1

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.2
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• pp.51-63
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• 2024

While the government policy to fully adopt BIM in the construction sector is being implemented, the construction and utilization of landscape BIM models are facing challenges due to problems such as limitations in BIM authoring tools, difficulties in modeling natural materials, and a shortage in BIM content including libraries. In particular, plants, fundamental design elements in the field of landscape architecture, must be included in BIM models, yet they are often omitted during the modeling process, or necessary information is not included, which further compromises the quality of the BIM data. This study aimed to contribute to the construction and utilization of landscape BIM models by developing a plant library that complies with BIM standards and is applicable to the landscape industry. The plant library of trees and shrubs was developed in Revit by modeling 3D shapes and collecting attribute items. The geometric information is simplified to express the unique characteristics of each plant species at LOD200, LOD300, and LOD350 levels. The attribute information includes properties on plant species identification, such as species name, specifications, and quantity estimation, as well as ecological attributes and environmental performance information, totaling 24 items. The names of the files were given so that the hierarchy of an object in the landscape field could be revealed and the object name could classify the plant itself. Its usability was examined by building a landscape BIM model of an apartment complex. The result showed that the plant library facilitated the construction process of the landscape BIM model. It was also confirmed that the library was properly operated in the basic utilization of the BIM model, such as 2D documentation, quantity takeoff, and design review. However, the library lacked ground cover, and had limitations in those variables such as the environmental performance of plants because various databases for some materials have not yet been established. Further efforts are needed to develop BIM modeling tools, techniques, and various databases for natural materials. Moreover, entities and systems responsible for creating, managing, distributing, and disseminating BIM libraries must be established.

• Journal of Environmental Impact Assessment
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• v.33 no.4
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• pp.143-154
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• 2024

Heat waves caused by climate change are rapidly increasing health damage to vulnerable groups, and to prevent this, the national, regional, and local governments are establishing climate crisis adaptation policy. A representative climate crisis adaptation policy to reduce heat wave damage is to expand the number of cooling centers. Because it is highly effective in a short period of time, most metropolitan local governments, except Jeonbuk, include the project as an adaptation policy. However, the criteria for selecting a cooling centers are different depending on the budget and non-budget, so the utilization rate and effectiveness of the cooling centers are all different. Therefore, in this study, we developed logistic regression models that can predict and evaluate areas with a high probability of expanding cooling centers in order to implement adaptation policy in local governments. In Incheon Metropolitan City, which consists of various heat wave-vulnerable environments due to the coexistence of the old city and the new city, a logistic model was developed to predict areas where heat waves can be cooling centered by dividing it into Ganghwa·Ongjin-gun and other regions, taking into account socioeconomic and environmental differences. As a result of the study, the statistical model for the Ganghwa·Ogjin-gun region showed that the higher the ground surface temperature and the more and more the number of elderly people over 65 years old, the higher the possibility of location of cooling centers, and the prediction accuracy was about 80.93%. The developed logistic regression model can predict and evaluate areas with a high potential as cooling centers by considering regional environmental and social characteristics, and is expected to be used for priority selection and management when designating additional cooling centers in the future.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.5 s.108
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• pp.430-439
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• 2006

This paper presents the a measured path-loss characteristics for mobile communications beyond 3G in microcellular residential area and street microcell at 3.4, 5.3, and 6.4 GHz band signals. The residential area is divided into two sections, one of which is composed of fifteen-story appartment buildings. The other section comprises four-story houses. The street microcell is classified line-of-sight(LOS) and nonline-of-sight(NLOS) areas. Both residential areas have standard deviations independent of the residential area classification, whereas the path loss exponents in the apartments is higher than those in area for same frequencies. A two-ray model is applied to analyse the path-loss charateristics in LOS areas. In LOS areas, an empirical breakpoint, whose distance is 6 percent shorter than a theorical breakpoint, is founded. Further, a sudden power level drop occurs at a transition point from LOS region to NLOS area. Path loss exponent is found to be significantly higher for non-LOS region than for LOS region. The power level drop due to corner loss and path-loss exponents both increase as the distance between the transmitter and the corner increases.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.