• 산업경영시스템학회지
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• 제18권35호
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• pp.115-122
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• 1995

This paper is developed with software system for the construction of periodic maintenance. The system includes records of equipment, maintenance work, failure mode analysis and work standards of maintenability, inspection & repair to establish periodic maintenance system. And the software program is designed with user-oriented to analyze maintenance data and maintenance system of periodic interval times. Also machine operator can easily apply maintenance management system in production & manufacturing field. Visual Basic in the environment of Window system is used as computer program language for graphics and data base management in IBM PC.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2014년도 춘계 학술논문 발표대회
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• pp.18-19
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• 2014

As issued the development of energy resources, EPC work process through the IT technology is essential for efficient business management, and systematic management of data generated in this process is needed. In domestic, the research related to system development for the collection and management of construction data detected in the field has been done continuously, but pipeline business target the long-distance in extreme cold weather, almost no cases have been studied up to now. Therefore, this research is aimed to derive the data item for efficient management in EPC Stage of pipeline business in extreme cold weather. WBS system of EPC work are classified easily at two levels, data items can be divided based on the type of document. In the future I will be expected to be the foundation of the systematic management of data generated in the EPC step-by-step of pipeline business.

• 한국건축시공학회지
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• 제11권3호
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• pp.238-246
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• 2011

Choosing the best-performance materials is a crucial task for the successful completion of a project in the construction field. In general, the process of material selection is performed through the use of information by a highly experienced expert and the purchasing agent, without the assistance of logical decision-making techniques. For this reason, the construction field has considered various artificial intelligence (AI) techniques to support decision systems as their own selection method. This study proposes the application of a systematic and efficient support vector machine (SVM) model to select optimal exterior materials. The dataset of the study is 120 completed construction projects in South Korea. A total of 8 input determinants were identified and verified from the literature review and interviews with experts. Using data classification and normalization, these 120 sets were divided into 3 groups, and then 5 binary classification models were constructed in a one-against-all (OAA) multi classification method. The SVM model, based on the kernel radical basis function, yielded a prediction accuracy rate of 87.5%. This study indicates that the SVM model appears to be feasible as a decision support system for selecting an optimal construction method.

• 한국건설관리학회논문집
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• 제5권2호
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• pp.153-161
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• 2004

대형 건설프로젝트의 효율적인 운용을 위해서는 프로젝트 각 분야간의 유기적인 관리체계가 필수적이라 할 수 있다. 대형 건설프로젝트의 관리업무 중 일정, 비용, 자재, 노무관리업무는 상호 유기적 종합관리를 가능하게 하는 업무들이며 이들을 종합적으로 $분석\cdot평가$할 수 있는 데이터 중심의 정보교환체계가 필수적이라 할 수 있다. 건설프로젝트관리의 $비용\cdot일정관리$업무에서 발생하는 자료의 중요성 및 활용빈도는 타 관리보다 높으며 건설공사의 효과적인 수행에 미치는 영향이 크다. $비용\cdot일정$ 통합데이터 통합관리를 통한 건설프로젝트관리체계를 수립하는 것은 생산성 측면과 자료 활용도 측면에서 더욱 효과적이라 할 수 있다. 본 논문에서는 $비용\cdot일정$ 통합 데이터베이스를 기반으로 하고 비용, 일정, 자재, 노무관리에 관련된 현장업무 분석을 통하여 대형건설공사의 프로세스 및 데이터 모델링을 통한 제반 필요정보의 분석 및 구조화를 제시하고자 한다.

• 한국지반신소재학회논문집
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• 제16권1호
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• pp.63-72
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• 2017

본 연구는 Shield TBM공법을 이용한 터널 굴착시 지표침하에 영향을 주는 요소들에 대한 영향정도를 3차원 수치해석을 통해 검토하였다. 막장압, Skinplate 주면압, 굴진장, 지반모델, 요소망 크기, 통과지반에 대한 다양한 조건을 변화시켜가며 수치해석을 수행하였다. 또한 실제 시공된 Shield TBM을 변위제어방법과 응력제어방법으로 모델링하여 현장 계측값과 비교 분석하였다. Skinplate 주면압과 지반모델이 가장 큰 영향요소이며, 통과지층에 따라 적절한 Skinplate 주면압을 입력시 현장 계측값과 유사한 결과를 얻을 수 있었다.

• 한국실내디자인학회논문집
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• 제16호
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• pp.75-81
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• 1998

With the rise of the economic level and the improvement of the standard of living the size of the interior work is becoming large and specialized, With the recent opening of the domestic interior decoration market the order of the large-scale interior decoration work is actively received and its efficient construction is vigorously made. Accordingly reliability is required in keeping with all the accuracy of computing interior construction expenses systematically is importantly emerging. The estimation sheet written in a kind of process mode and in an area made as the construction expense breakdown mode were statistically treated and analyzed as well as quantity computation breakdown data. In determing the major factors that expert an influence on the factors of changes in construction expenses as well as the compositional ratio of construction work that becomes basic material for developing the cost model of interior decoration work the following conclusion could be made: Improvement should be made to suit the present situation by synthesizing and arranging the data practically used in current interior construction expenses. Required construction expenses for the kind of work common to each construction field are showing a given proportion and the required construction expenses of rather small scale interior construction work tend to be irregular. It is necessary to compute optimal construction expenses by calculating the optimal period of work and working personnel in consideration of the influential factor in each work.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.736-743
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• 2022

Construction workers experience a high rate of fatal incidents from mobile equipment in the industry. One of the major causes is the decline in the acoustic condition of workers due to the constant exposure to construction noise. Previous studies have proposed various ways in which audio sensing and machine learning techniques can be used to track equipment's movement on the construction site but not on the audibility of safety signals. This study develops a novel framework to help automate safety surveillance in the construction site. This is done by detecting the audio sound at a different signal-to-noise ratio of -10db, -5db, 0db, 5db, and 10db to notify the worker of imminent dangers of mobile equipment. The scope of this study is focused on developing a signal processing model to help improve the audible sense of mobile equipment for workers. This study includes three-phase: (a) collect audio data of construction equipment, (b) develop a novel audio-based machine learning model for automated detection of collision hazards to be integrated into intelligent hearing protection devices, and (c) conduct field experiments to investigate the system' efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.

• 한국터널지하공간학회 논문집
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• 제23권6호
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• pp.559-575
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• 2021

최근지반굴착 공사의 증가로 인해 지반침하사고 발생 가능성은 점점 높아지고 있지만, 지하안전관리에 관한 특별법 제정 등 제도적인 강화에도 불구하고 지반침하사고를 근본적으로 예방하는 것은 매우 어려운 실정이다. 도심지 지반굴착의 여러 사례를 살펴보면, 굴착 전에 다양한 정보를 활용해 예측했던 지반침하 거동특성은 시공 중에 확인된 결과와 무시할 수 없을 정도의 차이를 보이고 있다. 이러한 원인은 지반조건의 변화, 지반침하 예측방법의 한계, 설계와 착공 시기의 계절적인 차이, 현장여건을 고려한 공법의 변경 및 기타 다양한 사유에 의한 장기간의 공사 중지 등의 현장 여건 변경이 주요 원인으로 고려될 수 있다. 이에 대응하기 위한 방안으로, 다양한 시공정보를 통한 안전관리시스템 도입을 예를 들 수 있으나 아직까지는 굴착으로 인한 영향 및 위험도를 예측할 수 있는 시스템은 부재한 실정이다. 본 연구에서는 도심지 굴착사업의 설계·시공에 있어서 지반침하와 주변 구조물에 미치는 굴착 영향도를 사전에 예측하고 위험도 평가를 할 수 있는 시스템을 개발하였으며, 과거에 획득된 현장계측 데이터를 통해 현재 및 장래 지하수위와 침하량 등을 예측할 수 있는 시계열 분석기법과 지반공학 데이터 시각화(Geotechnical Data Visualization, GDV) 기술을 적용하였다. 본 연구에서 개발된 웹기반 평가시스템을 통해 과거에 획득된 데이터를 이용하여 현재 및 장래 지하수위 변화 및 침하량 예측 등 굴착으로 인한 위험도 예측 및 관리가 가능할 것으로 기대된다.

• Journal of Ecology and Environment
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• 제47권4호
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• pp.187-192
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• 2023

The United States (US) National Science Foundation's (NSF's) National Ecological Observatory Network (NEON) is a continental-scale observation facility, constructed and operated by Battelle, that collects long-term ecological data to better understand and forecast how US ecosystems are changing. All data and samples are collected using standardized methods at 81 field sites across the US and are freely and openly available through the NEON data portal, application programming interface (API), and the NEON Biorepository. NSF led a decade-long design process with the research community, including numerous workshops to inform the key features of NEON, culminating in a formal final design review with an expert panel in 2009. The NEON construction phase began in 2012 and was completed in May 2019, when the observatory began the full operations phase. Full operations are defined as all 81 NEON sites completely built and fully operational, with data being collected using instrumented and observational methods. The intent of the NSF is for NEON operations to continue over a 30-year period. Each challenge encountered, problem solved, and risk realized on NEON offers up lessons learned for constructing and operating distributed ecological data collection infrastructure and data networks. NEON's construction phase included offices, labs, towers, aquatic instrumentation, terrestrial sampling plots, permits, development and testing of the instrumentation and associated cyberinfrastructure, and the development of community-supported collection plans. Although colocation of some sites with existing research sites and use of mostly "off the shelf" instrumentation was part of the design, successful completion of the construction phase required the development of new technologies and software for collecting and processing the hundreds of samples and 5.6 billion data records a day produced across NEON. Continued operation of NEON involves reexamining the decisions made in the past and using the input of the scientific community to evolve, upgrade, and improve data collection and resiliency at the field sites. Successes to date include improvements in flexibility and resilience for aquatic infrastructure designs, improved engagement with the scientific community that uses NEON data, and enhanced methods to deal with obsolescence of the instrumentation and infrastructure across the observatory.

• 한국지반환경공학회 논문집
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• 제13권8호
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• pp.25-33
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• 2012

터널 설계 시 해당지반에 관한 정보를 정확히 반영하는 것은 대단히 중요하다. 하지만 다양한 지형 및 지질조건을 모두 고려한 지반조사 및 시험 등은 경제적, 기술적으로 인하여 현실적으로 실시하기 어렵기 때문에 한정된 정보에 의하여 해석 및 설계를 하고 있는 실정이다. 본 연구는 도심지 및 산악지역 터널공사 시, 보다 정확한 안정성 검토 및 거동 예측을 수행하여 선정 결과에 대한 현장 적용성 여부를 판단하기 위해 인공신경망 이론의 적용을 통하여 기존 거동예측의 한계성을 극복하고자 하였다. 먼저, 현장 데이터를 확보하여 인공신경망 중 다층퍼셉트론을 연구에 적합한 구조로 구축하고, 역전파 알고리즘으로 학습시켜 적용하였다. 인공신경망을 이용한 현장적용성의 학습을 위한 자료는 터널의 지보패턴, RMR, Q, 암종, 굴진장, 굴착형태, 굴착경과일 등 터널거동에 영향을 미치는 영향인자를 고려하여 신뢰성 분석을 실시하고 선별된 계측자료의 결과를 데이터베이스화하여 사용하였다. 학습이 완료된 인공신경망 모델을 이용하여 터널시공현장의 굴착경과일에 따른 천단변위, 내공변위, 지중변위, 록볼트축력을 예측하고 현장 계측치와 비교분석을 통하여 인공신경망을 이용한 터널 시공 시 현장적용성을 확인하였다.