• International conference on construction engineering and project management
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• 2020.12a
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• pp.343-352
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• 2020

Modular integrated construction (MiC) is an innovative construction method where components of a building are manufactured in an offsite factory, trucked to the job site in sections, set in place with cranes, and assembled together to form a whole building. Where circumstances merit, favorable conditions exist and implemented effectively; MiC improves project performance. However, several key factors need to converge during implementation to realize the full benefits of MiC. Thus, a thorough understanding of the factors which are critical to the success of MiC projects is imperative. Drawing on a systematic review of 47 empirical studies, this research identified 25 key success factors (KSFs) for MiC projects. Of these, the five topmost cited KSFs for MiC projects include effective working collaboration and communication among project participants; standardization, optimization, automation and benchmarking of best practices; effective supply chain management; early design freeze and completion; and efficient procurement method and contracting. The study further proposed a conceptual model of the KSFs, highlighting the interdependences of people, processes, and technology-related KSFs for the effective accomplishment of MiC projects. The set of KSFs is practically relevant as they constitute a checklist of items for management to address and deal with during the planning and execution of MiC projects. They also provide a useful basis for future empirical studies tailored towards measuring the performance and success of MiC projects. MiC project participants and stakeholders will find this research useful in reducing failure risks and achieving more desired performance outcomes. One potential impact of the study is that it may inform, guide, and improve the successful implementation of MiC projects in the construction industry. However, the rigor of the analysis and relative importance ranking of the KSFs were limited due to the absence of data.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Korean Journal of Construction Engineering and Management
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• v.9 no.4
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• pp.92-100
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• 2008

Productivity is acknowledged as a very important factor for successful construction projects. Various data items collected daily form a construction site can be used for monitoring its productivity by analyzing them. However, no analytical methods for that purpose have been established in the domestic construction industry yet. Previous researches that utilized OLAP and data mining to analyze the factors that affect the productivity did not do well with predicting future cases with sufficient reliability. This research therefore proposes a new analytical process which is capable of figuring out the factors that would affect the productivity of future projects, through qualitative and quantitative analysis of the data collected from past projects.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.21-29
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• 2020

The Topic Modeling research, the methodology for deduction keyword within literature, has become active with the explosion of data from digital society transition. The research objective is to investigate research trends in D.N.A.(Data, Network, Artificial Intelligence) field using DTM(Dynamic Topic Model). DTM model was applied to the 1,519 of research projects with SW·A.I technology classifications among ICT(Information and Communication Technology) field projects between 6 years(2015~2020). As a result, technology keyword for D.N.A. field; Big data, Cloud, Artificial Intelligence, extended keyword; Unstructured, Edge Computing, Learning, Recognition was appeared every year, and accordingly that the above technology is being researched inclusively from other projects can be inferred. Finally, it is expected that the result from this paper become useful for future policy·R&D planning and corporation's technology·marketing strategy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.6
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• pp.1634-1647
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• 2007

"To invest or not to invest?" Most business leaders are frequently faced with this question on new and ongoing projects. The challenge lies in deciding what projects to choose, expand, contract, defer, or abandon. The project valuation tools used in this process are vital to making the right decisions. Traditional tools such as discounted cash flow (DCF)/net present value (NPV) assume a "fixed" path ahead, but real world projects face uncertainties, forcing us to change the path often. Comparing to other traditional valuation methods, the real options approach captures the flexibility inherent to investment decisions. The use of real options has gained wide acceptance among practitioners in a number of several industries during the last few decades. Even though the options are present in all types of business decisions, it is still not considered as a proper method of valuation in some industries. Mining has been comparably slow to adopt new valuation techniques over the years. The reason fur this is not entirely clear. One possible reason is the level and types of risks in mining. Not only are these risks high, but they are also more numerous and involve natural risks compared with other industries. That is why the purpose of this study is to deal with a more practical approach to project valuation, known as real options analysis in mining industry. This paper provides a case study approach to the copper mining industry using a real options analysis. It shows how companies can minimize investment risks, exercise flexibility in decision making and maximize returns.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.2
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• pp.150-161
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• 2015

This paper proposes a new approach to technology valuation, the market-replacement cost approach which integrates the cost-based approach and market-based approach. The proposed approach estimates the market-replacement cost of a target technology using R&D costs of similar R&D projects previously conducted. Similar R&D projects are extracted from project database based on document similarity between project proposals and technology description of the target technology. R&D costs of similar R&D projects are adjusted by mirroring the rate of technological obsolescence and inflation. Market-replacement cost of the technology is then derived by calculating the weighted average of adjusted costs and similarity values of similar R&D projects. A case of "Prevention method and system for the diffusion of mobile malicious code" is presented to illustrate the proposed approach.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.1-10
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• 2018

Current evaluation practices for IT projects suffer from several problems, which include the difficulty of self-explanation for the evaluation results and the improperly scaled scoring system. This study aims to develop a methodology of opinion mining to extract key factors for the causal relationship analysis and to assess the feasibility of quantifying evaluation scores from text comments using opinion mining based on big data analysis. The research has been performed on the domain of publicly procured IT proposal evaluations, which are managed by the National Procurement Service. Around 10,000 sets of comments and evaluation scores have been gathered, most of which are in the form of digital data but some in paper documents. Thus, more refined form of text has been prepared using various tools. From them, keywords for factors and polarity indicators have been extracted, and experts on this domain have selected some of them as the key factors and indicators. Also, those keywords have been grouped into into dimensions. Causal relationship between keyword or dimension factors and evaluation scores were analyzed based on the two research models-a keyword-based model and a dimension-based model, using the correlation analysis and the regression analysis. The results show that keyword factors such as planning, strategy, technology and PM mostly affects the evaluation result and that the keywords are more appropriate forms of factors for causal relationship analysis than the dimensions. Also, it can be asserted from the analysis that evaluation scores can be composed or calculated from the unstructured text comments using opinion mining, when a comprehensive dictionary of polarity for Korean language can be provided. This study may contribute to the area of big data-based evaluation methodology and opinion mining for IT proposal evaluation, leading to a more reliable and effective IT proposal evaluation method.

• International Journal of Advanced Culture Technology
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• v.8 no.1
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• pp.82-88
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• 2020

In the face of recent digital and digital transformation, companies and industries are trying to be agile to adapt and respond to change. Agile paradigm is spreading beyond the boundaries of existing applications such as IT-related projects and software development. In this regard, this study, we analyzed the diffusion of agile paradigm by text mining abstracts of research papers from 2001 to 2019. In addition, we discussed agile transformation in the Fourth Industrial Revolution. Through this study, we confirmed that we are studying agile transformation in various fields such as business environment, corporate organizational culture, manufacturing industry, and supply chain. The results of this study will contribute to understanding the meaning and role of agile as a basic paradigm for digital transformation in the Fourth Industrial Revolution.

• Journal of Environmental Impact Assessment
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• v.10 no.1
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• pp.49-57
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• 2001

With reducing coal mining industry the number of coal mine sites between 1988 and 1998 was dropped from 347 to 12. Since the abandoned coal mine sites have been kept without any cares, they have raised various environmental and safety problems. Then, Korean government initiated a special law in 1995 for enhancing economic conditions and solving environmental problems with promoting developmental projects in the abandoned mining sites. As a result, casino business in Chungsun area has been opened to publics, and other large-scale developments such as ski slopes and resorts are planned. In addition, Boryung area in Chungchung province also will launch a large-scale project building golf courses. Based on this developmental trend, it is expected that lots of large-scale developments in other places will be taken place. In general, the large-scale developments have caused various environmental problems, and, thus, environmental aspects should be considered in a decision-making process. This paper examine the status of the abandoned mine sites in Korea and U.S. and suggests the alternatives of its utilization.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.42-51
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• 2003

The global demand for underground facilities has increased substantially in the past decades, and a substantial number of underground projects have had to deal with challenging ground conditions in urban environments. Particularly challenging are weak and unstable water bearing soils. Advancements in shielded TBM tech-nology have led to significant improvements regarding the ability to control ground deformations in soft ground. Nonetheless, ground collapse may occur even when the most advanced TBM designs are employed if unexpected adverse ground conditions are encountered or if insufficient stabilizing pressure is transferred to the tunnel face. This paper reviews common approaches for face stability and face pressure transmission calculations, and provides an overview of some of the latest technological developments and considerations for soft ground TBM applica-tions.