• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.321-323
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• 2015

Monitoring crane motion in real time is the first step to identifying and mitigating crane-related hazards on construction sites. However, no accurate and reliable crane motion capturing technique is available to serve this purpose. The objective of this research is to explore a method for real-time crane motion capturing and investigate an approach for assisting hazard detection. To achieve this goal, this research employed various techniques including: 1) a sensor-based method that accurately, reliably, and comprehensively captures crane motions in real-time; 2) computationally efficient algorithms for fusing and processing sensing data (e.g., distance, angle, acceleration) from different types of sensors; 3) an approach that integrates crane motion data with known as-is environment data to detect hazards associated with lifting tasks; and 4) a strategy that effectively presents crane operator with crane motion information and warn them with potential hazards. A prototype system was developed and tested on a real crane in a field environment. The results show that the system is able to continuously and accurately monitor crane motion in real-time.

• Structural Monitoring and Maintenance
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• 제10권3호
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• pp.191-205
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• 2023

In this paper, through operational modal analysis and ambient vibration tests, the dynamic characteristics of a multi-span simply-supported reinforced concrete highway bridge deck was determined and the results were used to assess the quality of construction of the individual spans. Supporting finite element (FE) models were created and analyzed according to the design drawings. After carrying out the dynamic tests and extracting the modal properties of the deck, the quality of construction was relatively assessed by comparing the results obtained from all the tests from the individual spans and the FE results. A comparison of the test results among the different spans showed a maximum difference value of around 9.3 percent between the superstructure's natural frequencies. These minor differences besides the obtained values of modal damping ratios, in which the differences were not more than 5 percent, can be resulted from suitable performance, health, and acceptable construction quality of the bridge.

• 한국BIM학회 논문집
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• 제11권2호
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• pp.24-32
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• 2021

Recently, starting with smart construction research, interest in technology that automates construction site management using artificial intelligence technology is increasing. In order to automate construction site management, it is necessary to recognize objects such as construction equipment or workers, and automatically analyze the relationship between them. For example, if the relationship between workers and construction equipment at a construction site can be known, various use cases of site management such as work productivity, equipment operation status monitoring, and safety management can be implemented. This study derives a real-time object detection platform architecture that is required when performing construction site management using deep learning technology, which has recently been increasingly used. To this end, deep learning models that support real-time object detection are investigated and analyzed. Based on this, a deep learning model development process required for real-time construction site object detection is defined. Based on the defined process, a prototype that learns and detects construction site objects is developed, and then platform development considerations and architecture are derived from the results.

• 국제학술발표논문집
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• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
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• pp.58-65
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• 2011

Construction Productivity is one of the most important elements in construction management. It is used in construction process scheduling and cost management, which are significant sector in construction management. It is important to make appropriate schedule and monitor how works are done within schedule. But construction project contains uncertainty and inexactitude, modifying construction schedule is being an issue to manage construction works well. Even though prediction and monitoring of productivity can be principal activity, it is hard to predict productivity with manager's experience and a standard of estimate. A large number of factors influencing productivity, such as drawing, construction method, weather, labor, material, equipment, etc. But current calculation of productivity depends on empirical probability, not consider difference of each influencing factor. In this research, the aim is to present a productivity predicting regression model of form work, which includes effectiveness of influences factors. 5 variables existed inside form work are selected by interview and site research based on literature review of existed various productivity influencing factors. The effectiveness and correlation of productivity influencing factors are analyzed by statistical approach, and it is used to make productivity regression model. The finding of this research will improves monitoring and controlling of project schedule in construction phase.

• 국제초고층학회논문집
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• 제4권1호
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• pp.27-37
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• 2015

Structural health monitoring is becoming more and more important in the domain of civil engineering as a proper mean to increase and maintain the safety, especially in the land of earthquakes like Japan. In many civil structures, the deformations are the most relevant parameter to be monitored. In this context, a monitoring technology based on the use of long-gage fiber optic deformation sensor, SOFO is being applied to a 33-floors tall building in Tokyo. Sensors were installed on the $2^{nd}$ floor's steel columns of the building on May 2005 in the early stage of the construction. The installed SOFO sensors were dynamic compatible ones which enable both static and dynamic measurements. The monitoring is to be performed during the whole lifespan of the building. During the construction, static deformations of the columns had been measured on a regular basis using a reading unit for static measurement and dynamic deformation measurements were occasionally conducted using a reading unit for dynamic measurement. The building was completed on August 2006. After the completion, static and dynamic deformation measurements have been continuing. This paper describes a health monitoring technology, SOFO system which is applicable to high-rise buildings and monitoring results of a 33-floors tall building in Tokyo from May 2005 to October 2010.

• Smart Structures and Systems
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• 제26권1호
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• pp.11-21
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• 2020

The hinge joint is the key to the overall cooperative working performance of the assembled beam bridge, and it is also the weakest part during the service period. This paper proposes a method for monitoring and evaluating the lateral cooperative working performance of fabricated beam bridges based on dynamic strain correlation coefficient indicator. This method is suitable for monitoring and evaluation of hinge joints status between prefabricated girders and overall cooperative working performance of bridge, without interruption of traffic and easy implementation. The remote cloud monitoring and diagnosis system was designed and implemented on a real assembled beam bridge. The algorithms of data preprocessing, online indicator extraction and status diagnosis were given, and the corresponding software platform and scientific computing environment for cloud operation were developed. Through the analysis of real bridge monitoring data, the effectiveness and accuracy of the method are proved and it can be used in the health monitoring system of such bridges.

• 인터넷정보학회논문지
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• 제17권6호
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• pp.61-69
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• 2016

테스팅은 시스템의 요구사항을 바탕으로 테스트 케이스를 생성하여 소프트웨어에 내재되어 있는 결함을 발견하는 활동이다. 테스팅을 효과적으로 수행하기 위해서는 충실한 테스트 계획, 잘 작성된 테스트 케이스 생성과 더불어 체계적인 테스트 모니터링 활동이 요구된다. 테스트 자동화 방법에 대한 대부분의 연구들은 테스트 케이스 생성에서 테스트 실행까지의 자동화 방법에 초점이 맞춰져 있다. 본 연구에서는 이와 달리 테스트 모니터링의 자동화 방안에 대하여 연구한다. 이를 위해 테스트 모니터링 자동화를 위해 해결해야 할 요소를 도출하고 이를 기반으로 테스트 모니터링 자동화 프레임워크의 구축 방안을 제시한다.

• 한국건축시공학회지
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• 제9권4호
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• pp.103-109
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• 2009

건설산업은 전체산업분야들 중에서 가장 많은 재해사망자를 발생시키는 산업분야이다. 또한 재해건수와 재해자수가 감소하고 있는 타 산업분야와는 달리, 건설산업에서는 매년 재해건수와 재해자수가 증가하고 있는 실정이다. 한편 건설재해는 다양한 잠재 요인들이 서로 연관 축적되다가 한계에 이르게 되면 어느 한 순간에 갑작스럽게 발생하는 특징과 함께 다양한 재해가 연속적 복합적으로 일어나는 특성을 가지고 있다. 따라서 기존의 안전관리방법 만으로 안전사고를 예방하는 데에는 한계가 드러나고 있다. 본 연구의 목적은 건축공사현장에서의 안전관리에 USN기술의 접목 가능성을 검토하고, 안전관리 모니터링을 위한 USN기술 적용방안을 도출하는 것이다. 본 연구에서는 우선 기존 건설공사현장에서의 안전관리 업무를 분석하고, 재해율을 분석하였다. 그리고 건설공사현장의 재해 특성을 분석하여 건설현장 안전관리 모니터링의 중점관리요소와 차순위 관리요소들을 도출하였다. 마지막으로 USN기술을 적용한 건축공사현장 안전관리 모니터링 시스템의 구성과 모니터링 시스템의 흐름을 제시하였다. 이러한 첨단 USN기술의 건설분야 접목을 통하여 건설현장 근로자의 안전사고를 미연에 방지할 수 있을 것으로 기대된다.

• 한국재난정보학회 논문집
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• 제20권1호
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• pp.106-116
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• 2024

연구목적: 본 논문은 건설 시공현장에서 발생하는 사고 및 잠재적 위험분석을 위한 IoT 및 CCTV 기반 안전모니터링을 실시하고 추락, 충돌 등 위험 또는 이상현상을 탐지하여 무전기 등을 이용한 예·경보 및 챗봇서비스를 구축하는 방법을 제시하는데 목적이 있다. 연구방법: 건설현장 스마트 건설기술 사례 및 문헌분석을 통하여 안전관리 모델을 제시한다. 연구결과: '건설사고 통계'에 따르면 2021년 건설업 사고재해자는 26,888명으로 전체 사고재해의 26.3%가 건설업에서 발생하였고, 건설업 안전사고 사망자는 417명으로 전체 산업재해 사망자의 50.5%에 달한다. 이런한 건설재해의 개선 방안으로, IoT 건전성모니터링 기반 스마트 건설기술을 활용한 건설현장 안전관리 AI 챗봇서비스를 제시한다. 근로자 등 이해관계자가 참여하는 건설현장은 비계공정 및 개구부, 위험기계기구류 접근 등 사업장 내부 주요 위험구역을 선정하여 인공지능 챗봇시스템을 구현하여 실증하였다. 결론: 건설현장 인공지능 챗봇서비스 실증결과에 대한 참여근로자의 만족도 조사에서 90점 이상을 받아 상업화 가능성을 확인하였다.

• 건설안전기술
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• 통권44호
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• pp.80-94
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• 2008