• Title/Summary/Keyword: Model Of Building Information Management

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Scorm-based Sequencing & Navigation Model for Collaborative Learning (Scorm 기반 협력학습을 위한 시퀀싱 & 네비게이션 모델)

  • Doo, Chang-Ho;Lee, Jun-Seok
    • Journal of Digital Convergence
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    • v.10 no.6
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    • pp.189-196
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    • 2012
  • In this paper, we propose a Scorm-based Sequencing & Navigation Model for Collaborative Learning. It is an e-Learning process control model that is used to efficiently and graphically defining Scorm's content aggregation model and its sequencing prerequistites through a formal approach. To define a process based model uses the expanded ICN(Information Control Net) model. which is called SCOSNCN(SCO Sequencing & Navigation Control Net). We strongly believe that the process-driven model delivers a way of much more convenient content aggregating work and system, in terms of not only defining the intended sequence and ordering of learning activities, but also building the runtime environment for sequencing and navigation of learning activities and experiences.

A Neural Network Model for Selecting a Piling Method of Building Construction (건축공사 말뚝공법 선정을 위한 신경망 모델 개발)

  • Cheon Bong-Ho;Koo Choong-Wan;Um Ik-Joon;Koo Kyo-Jin
    • Proceedings of the Korean Institute Of Construction Engineering and Management
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    • 2004.11a
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    • pp.317-322
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    • 2004
  • As a construction project in urban area tends to be high-rise and huge, the importance of the project's underground work, in terms of the cost and the schedule, is gradually increasing. It's extremely significant to choose a proper filing method, at the stage of underground work. However, in piling work many change orders have been occurred since a piling method is experientially selected based on uncertain information and many earth factors to consider. It has effects on the cost and the schedule of the project. In this study, we have suggested a decision model for piling method that can be used to determine and verify the suitable piling method in design and pre-construction phase of a project. Based on historical data, a neural network model has already proven to be efficient. The tests of the model for selecting a suitable piling method have progressed exactly with the data of 150 piling works which were done room 2000 to 2004 in Korea. The optimization or the developed neural network model has progressed with the data for teaming. The validity of the neural network model has been verified.

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An Evaluative Study of the Operational Safety of High-Speed Railway Stations Based on IEM-Fuzzy Comprehensive Assessment Theory

  • Wang, Li;Jin, Chunling;Xu, Chongqi
    • Journal of Information Processing Systems
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    • v.16 no.5
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    • pp.1064-1073
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    • 2020
  • The general situation of system composition and safety management of high-speed railway terminal is investigated and a comprehensive evaluation index system of operational security is established on the basis of railway laws and regulations and previous research results to evaluate the operational security management of the high-speed railway terminal objectively and scientifically. Index weight is determined by introducing interval eigenvalue method (IEM), which aims to reduce the dependence of judgment matrix on consistency test and improve judgment accuracy. Operational security status of a high-speed railway terminal in northwest China is analyzed using the traditional model of fuzzy comprehensive evaluation, and a general technique idea and references for the operational security evaluation of the high-speed railway terminal are provided. IEM is introduced to determine the weight of each index, overcomes shortcomings of traditional analytic hierarchy process (AHP) method, and improves the accuracy and scientificity of the comprehensive evaluation. Risk factors, such as terrorist attacks, bad weather, and building fires, are intentionally avoided in the selection of evaluation indicators due to the complexity of risk factors in the operation of high-speed railway passenger stations and limitation of the length of the paper. However, such risk factors should be considered in the follow-up studies.

Software-In-the-Loop based Power Management System Modeling & Simulation for a Liquefied Natural Gas Carrier (SIL 기반 액화천연가스운반선 전력관리시스템의 모델링 및 시뮬레이션)

  • Lee, Kwangkook
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.21 no.6
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    • pp.1218-1224
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    • 2017
  • With the increasing risk in building liquefied natural gas carriers (LNGC), pre-simulation of various scenarios is required for system integration and safe operation. In particular, the power management system (PMS) is an important part of the LNGC; it works in tight integration with the power control systems to achieve the desired performance and safety. To verify and improve unpredicted errors, we implemented a simulation model of power generation and consumption for testing PMS based on software-in-the-loop (SIL) method. To control and verify the PMS, numeric and physical simulation modeling was undertaken utilizing MATLAB/Simulink. In addition, the simulation model was verified with a load sharing test scenario for a sea trial. This simulation allows shipbuilders to participate in new value-added markets such as commissioning, installation, operation, and maintenance.

Comparative Analysis of BIM Acceptance Factors between Korea and China (한국과 중국의 BIM 수용영향요인 비교분석)

  • Song, Jingxu;Lee, Seulki;Yu, Joungho
    • Korean Journal of Construction Engineering and Management
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    • v.22 no.6
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    • pp.3-14
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    • 2021
  • In the Chinese construction industry, the utilization of Building Information Modeling (BIM) aims to increase the total output of the construction industry by solving the problem of inefficient interoperability in the construction industry. In 2011, the Chinese Ministry of Housing and Urban-Rural Development despite the technical advantages of BIM and the government policy, the BIM adoption rate in China is lower than 45%. Meanwhile, as the South Korean construction industry is a step ahead of its Chinese counterpart in introducing and utilizing BIM, it is expected that BIM is more actively utilized and accepted in South Korea than in China. According to a comparative study based on the hype-cycle theory, South Korea is at a more advanced stage of introducing BIM, than in China. This study aimed to suggest how to increase BIM utilization rates in China. To this end, this study comparatively analyzed factors affecting BIM acceptance between China and South Korea. For the comparative analysis of the BIM acceptance factors between China and South Korea, literature reviews on the technology acceptance model (TAM) and BIM acceptance model were carried out, and based on that, the BIM acceptance factors were classified. Other BIM acceptance factors were also added and considered, as they reflected Chinese national characteristics and construction industry. As for the derived BIM acceptance factors, construction project participants, especially actual BIM users in China and South Korea, were targeted for the survey. A t-test using SPSS 22.00 was carried out to identify significant differences in data. Finally, based on the t-test results, this study suggested ways of improving the BIM utilization rate in China. Based on the findings, this study is expected to contribute to activating BIM adoption in the Chinese construction industry and also to set a theoretical foundation for future studies on BIM utilization in the industry.

Radiation Flux Impact in High Density Residential Areas - A Case Study from Jungnang area, Seoul - (고밀도 주거지역에서의 복사플럭스 영향 연구 - 서울시 중랑구 지역을 대상으로 -)

  • YI, Chae-Yeon;KWON, Hyuk-Gi;Lindberg, Fredrik
    • Journal of the Korean Association of Geographic Information Studies
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    • v.21 no.4
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    • pp.26-49
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    • 2018
  • The purpose of this study was to verify the reliability of the solar radiation model and discuss its applicability to the urban area of Seoul for summer heat stress mitigation. We extended the study area closer to the city scale and enhanced the spatial resolution sufficiently to determine pedestrian-level urban radiance. The domain was a $4km^2$ residential area with high-rise building sites. Radiance modelling (SOLWEIG) was performed with LiDAR (Light Detection and Ranging)-based detailed geomorphological land cover shape. The radiance model was evaluated using surface energy balance (SEB) observations. The model showed the highest accuracy on a clear day in summer. When the mean radiation temperature (MRT) was simulated, the highest value was for a low-rise building area and road surface with a low shadow effect. On the other hand, for high-rise buildings and vegetated areas, the effect of shadows was large and showed a relatively low value of mean radiation temperature. The method proposed in this study exhibits high reliability for the management of heat stress in urban areas at pedestrian height. It is applicable for many urban micro-climate management functions related to natural and artificial urban settings; for example, when a new urban infrastructure is planned.

Automated Phase Identification in Shingle Installation Operation Using Machine Learning

  • Dutta, Amrita;Breloff, Scott P.;Dai, Fei;Sinsel, Erik W.;Warren, Christopher M.;Wu, John Z.
    • International conference on construction engineering and project management
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    • 2022.06a
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    • pp.728-735
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    • 2022
  • Roofers get exposed to increased risk of knee musculoskeletal disorders (MSDs) at different phases of a sloped shingle installation task. As different phases are associated with different risk levels, this study explored the application of machine learning for automated classification of seven phases in a shingle installation task using knee kinematics and roof slope information. An optical motion capture system was used to collect knee kinematics data from nine subjects who mimicked shingle installation on a slope-adjustable wooden platform. Four features were used in building a phase classification model. They were three knee joint rotation angles (i.e., flexion, abduction-adduction, and internal-external rotation) of the subjects, and the roof slope at which they operated. Three ensemble machine learning algorithms (i.e., random forests, decision trees, and k-nearest neighbors) were used for training and prediction. The simulations indicate that the k-nearest neighbor classifier provided the best performance, with an overall accuracy of 92.62%, demonstrating the considerable potential of machine learning methods in detecting shingle installation phases from workers knee joint rotation and roof slope information. This knowledge, with further investigation, may facilitate knee MSD risk identification among roofers and intervention development.

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EXPLORING THE KEY FACTORS FOR BIM ACCEPTANCE IN CONSTRUCTION ORGANIZATIONS

  • Seul-Ki Lee;Jung-Ho Yu
    • International conference on construction engineering and project management
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    • 2013.01a
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    • pp.14-20
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    • 2013
  • Substantial research has been performed on the data standards and exchanges in the AEC/FM industry over the past several years. The growing popularity of BIM technology is based heavily upon a perception that the technology can facilitate the sharing and reuse of information during a project life-cycle. Although many researchers and practitioners are in agreement about the potential applicability and benefit of BIM in construction, it is still unclear why BIM is adopted, and what factors enhance implementation of BIM. Thus, BIM acceptance and use remains a central concern of BIM research and practice. Therefore, we propose the key factors affecting the acceptance of BIM in construction organizations using factor analysis. The key factors for BIM acceptance are identified through a literature review in TAM (Davis 1989) and related theories, and consolidated by interviews and pilot studies with professionals in construction industry. Based on the factors, a questionnaire was designed and sent out to construction organizations such as contractors, architects, and engineers in Korea. Total 148 completed questionnaires were retrieved. Using factor analysis, key factors were grouped into six dimensions. These findings will clarify what the highly prioritized factors are, and can also be used in an assessment tool for the performance of BIM utilization.

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″Issues in designing a Knowledge-based system to support process modeling″

  • Suh, Eui-Ho;Kim, Suyeon
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2001.10a
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    • pp.50-54
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    • 2001
  • Information systems development entails planning, analysis, design and construction phases. The analysis phase identifying user requirements is the most important of these phases. Since unidentified defects in the early phase causes increased work and costs as development proceeds, the quality of analysis results affects the quality of the resultant system. Major tasks in the analysis phase are data modeling and process modeling. Research on building a knowledge-based system for data modeling have been conducted much, however, not sufficiently for process modeling. As a system environment with high user interaction increases, research on process modeling methods and knowledge- based systems considering such environment are required. In this research, a process modeling framework for information systems with high user interaction is suggested and a knowledge-based system for supporting the suggested framework is implemented. A proposed model consists of the following tasks: event analysis, process analysis, and event/process interaction analysis. Event analysis identifies business events and their responses. Process analysis break down the processes of an enterprise into progressively increasing details. Decomposition begins at the function level and ends when the elementary process level is reached. Event/process interaction analysis verifies the results of process analysis and event analysis. A knowledge-based system for supporting a proposed process modeling framework is implemented in a web-based environment.

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A Study on the Investment Strategy Using Neural Network Models in the Korean Stock Market (인공신경망 모델을 이용한 주식시장에서의 투자전략에 대한 연구)

  • 서영호;이정호
    • Journal of the Korean Operations Research and Management Science Society
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    • v.23 no.4
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    • pp.213-224
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    • 1998
  • Since the late 1980s, an Increasing number of neural network models have been studied in the areas of financial prediction and analysis. The purpose of this study is to Investigate the possibility of building a neural network model that is able to construct a profitable trading strategy in the Korean Stock Market. This study classifies stocks into the future market winners and losers from the publicly available accounting information and builds portfolios based on this information. The performances of the winner portfolios and the loser portfolios are compared with each other and against the market index. The empirical result of this research is consistent with the traditional fundamental analysis where it is claimed that the financial statements contain firm values that may not be fully reflected In stock prices without delay. Despite the supporting empirical evidence. It is somewhat Inconclusive as to whether or not the abnormal return in excess of market return is the result of the extra knowledge obtained in the neural network models derived from the historical accounting data. This research attempts to open another avenue using neural network models for searching for evidence against market efficiency where statistics and intuition have played a major role.

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