• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2011년도 춘계 학술논문 발표대회 2부
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• pp.111-114
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• 2011

Korea construction industry needs to secure its competitiveness in global construction markets and to urgently find out the proper approaches in consideration of the current situation of Korean construction enterprises and environmental changes in the international market. Korea has national plan to improve the competitive level of the construction industry. Expecially, Government is trying to build the global system in construction industry. But there are no the related national competitive date. Therefore, this paper suggested national competitive indicators and development direction in construction industry. And this research has presented the competitiveness analysis of construction industry with the consideration of condition of infrastructure, transparency and technological competitiveness.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 봄 학술논문 발표대회
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• pp.225-226
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• 2022

The construction industry, which has a labour-intensive and conservative nature, is exclusive to adopt new technologies. However, the construction industry is viably introducing the 4th Industrial Revolution technologies represented by artificial intelligence, Internet of Things, robotics and unmanned transportation to promote change into a smart industry. An image-based artificial intelligence technology is a field of computer vision technology that refers to machines mimicking human visual recognition of objects from pictures or videos. The purpose of this article is to explore image-based artificial intelligence technologies which would be able to apply to the construction sites. In this study, we show two examples which is one for a construction waste classification model and another for cast in-situ anchor bolts defection detection model. Image-based intelligence technologies would be used for various measurement, classification, and detection works that occur in the construction projects.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2023년도 가을학술발표대회논문집
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• pp.265-266
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• 2023

Recently, many disasters have occurred due to poor management of construction site. In addition, as legal regulations on safety management at construction sites are strengthened, its importance is being further emphasized. In relation to smart safety management technology, a study was introduced to build an analysis model through various safety-related data collected within construction companies. This model derives quantitative disaster risk about the site level through information related to past disasters and near misses. However, construction work is performed separately by work group of each partner company. There is a limitation in that individual workers cannot directly experience this analysis information. In this study, we propose a method to derive the safety disaster risk of individual work units from disaster risk of the site level. We expect that this study to be helpful for smart safety management technology of construction sites.

• 한국해양공학회지
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• 제27권4호
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• pp.33-44
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• 2013

IIn this study, a wave-surge-tide coupling numerical model was developed to consider nonlinear interaction. Then, this model was applied and calculations were made for a storm surge on the southeast coast. The southeast coast was damaged by typhoon "Maemi" in 2003. In this study, we used a nearshore wind wave model called SWAN (Simulating WAves Nearshore). In addition, the Meyer model was used for the typhoon model, along with an ocean circulation model called POM (Princeton Ocean Model). The wave-surge-tide coupling numerical model could calculate exact parameters when each model was changed to consider the nonlinear interaction.

• 한국건설관리학회논문집
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• 제19권5호
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• pp.43-52
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• 2018

일반적으로 공사비에 대한 연구는 직접비 위주로 행해졌으며, 간접비를 면밀하게 산정하는 모델에 대한 연구가 부족하다. 본 연구의 목적은 국내 건설시장에서 큰 축을 차지하는 아파트 건설공사 현장의 현장관리비를 예측하기 위한 모델을 제시하는 것이다. 아파트 건설공사 현장 다수의 전체공사기간 동안의 실비사용 데이터를 분석하여 곡선접합 분석을 통해 공정률별 1일당 현장관리비를 도출할 수 있는 9차방정식을 제안하였으며, 이를 활용하여 300억 규모의 공사의 경우의 현장관리비를 추정하는 결과를 보여줌으로서 활용가능성을 설명하고 있다. 선행연구에서는 총 현장관리비의 규모의 변화패턴을 직접적으로 확인할 수 있는 다항식을 도출한 사례는 없었던 점에 비추어 본다면, 본 연구에서 제시한 모델은 그 편의성과 면밀성에 합리적 근거를 토대로 현장관리비를 예측할 수 있다는 점에서 연구의 기여도가 있다.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.304-311
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• 2022

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.401-404
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• 2015

Demand on free-form buildings is gradually increasing, yet owing to the difficulty of production-installation work, several problems occur in the construction phase upon construction of a building, including the increased cost and construction duration, and reduced constructibility. To solve these problems, a techonology to produce FCP using a CNC(Computerized Numeric Control) machine is developed. The technology is that the information of designed free-form buildings to the CNC machine is transferred, and the transferred information is used for RTM(Rod-Type Mold, the mold shaped by back-up rods) and PCM(Phase Change Material) shaping, and the shaped RTM and PCM have the role of molds to produce FCP. Construction duration and project cost are limited in building sites, so the efficiency of processes like production-installation of FCP for application of the technology is significant. Since it is almost impossible to change the production-installation process at the construction phase when they are established, process should be deliberately decided. Therefore, the purpose of the study is to propose a production-installation simulation model of free-form concrete panels, in aspect of PCM. This paper is establishing the process for production-installation of FCP, estimating time required by each construction type and proposing a time simulation model that changes according to various constraints based on the analyses. With the time simulation model, it will be possible to build a cost model and to review the optimal construction duration and project cost.

• 한국건설관리학회논문집
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• 제24권5호
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• pp.22-34
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• 2023

건설사업의 사업초기단계에 산정되는 공사비 예산을 적절히 예측하는 것은 발주자의 올바른 의사결정을 지원하고 건설사업의 목표를 달성하기 위해 매우 중요한 현안이다. 이는 공공 건설사업의 경우에서도 마찬가지이다. 그러나 현재 공공 건설사업의 사업초기단계에서 수행되는 공사비 예산의 예측방식은 정확성 및 신뢰성 관점에서 정교하지 못해 이에 대한 개선의 필요성이 제기되고 있다. 본 연구의 목적은 인공신경망을 활용하여 공공청사 프로젝트 사업초기단계에서 활용할 수 있는 공사비 예산 예측모델을 개발하는데 있다. 본 연구에서는 조달청에서 제공하는 데이터와 SPSS Statistics 프로그램을 활용하여 인공신경망 모델을 구축하였으며, 공사비 예산 예측의 수준을 분석하고 추가 검증을 통해 모델의 정확성을 검증하였다. 검증 결과, 개발된 인공신경망 모델은 사업초기 단계에서 활용할 수 있는 견적의 오차범위를 보여주었으며 이를 통해 다양한 프로젝트 조건(변수)을 활용하여 보다 정교하게 공사비 예산을 예측할 수 있는 가능성을 시사하였다.

• 한국과학교육학회지
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• 제36권2호
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• pp.279-293
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• 2016

본 연구의 목적은 초등학교 6학년 1학기 3단원 렌즈의 이용단원에서 인포그래픽 수업을 이용하여 학생들이 과학적 모델 사용이 활성화 되는지 알아보는 것이다. 연구 대상은 경상남도 G시의 G초등학교 6학년 53명을 대상으로 실시하였다. 이 연구를 위해, 수업 계획은 학생들의 렌즈에 대한 선개념 조사, 과학적 모델 구성 활동, 인포그래픽 구성 활동으로 3단계로 구성하였다. 그리고 과학적 개념, 과학적 모델, 인포그래픽 3개의 관점으로 연구 결과를 분석하였다. 수업이 이루어지기 전, 학생들은 렌즈에 관해 렌즈의 외형적 형태와 구성물질에 주로 선개념을 가지고 있었다. 그러나 과학적 모델 구성 활동과 인포그래픽 구성 활동을 한 후에는 렌즈의 특징, 안경의 특징, 빛의 나아감, 렌즈의 이용 범주에서 개념적으로 향상된 것을 확인하였다. 과학적 모델 분석 관점에서는 과학적 모델 구성 활동보다 인포그래픽 구성 활동에서 다양한 종류의 과학적 모델이 사용되고 사용 빈도도 높게 나타났다. 또한 인포그래픽 분석 관점에서는 인포그래픽 구성 활동에서 인포그래픽이 아닌 그림보다 기능기반 인포그래픽과 관계기반 인포그래픽이 증가하였다. 그리고 게슈탈트의 시지각 특성의 빈도가 과학적 모델 구성 활동보다 인포그래픽 구성 활동에서 더 높게 나타났다.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.676-684
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• 2009

Decision making at the early stages of a construction project has a significant impact on the project, and various scenarios created based on the owner's requirements should be considered for the decision making. At the early stages of a construction project, the information regarding the project is usually limited and uncertain. As such, it is difficult to plan and manage the project (especially cost planning). Thus, in this study, a cost model that could be varied according to the owner's requirements was developed. The cost model that was developed in this study is based on the case-based reasoning (CBR) methodology. The model suggests cost estimation with the most similar historical case as a basis for the estimation. In this study, the optimization process was also conducted, using genetic algorithms that reflect the changes in the number of project characteristics and in the database in the model according to the owner's decision making. Two optimization parameters were established: (1) the minimum criteria for scoring attribute similarity (MCAS); and (2) the range of attribute weights (RAW). The cost model proposed in this study can help building owners and managers estimate the project budget at the business planning stage.