• 농업과학연구
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• 제42권2호
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• pp.117-124
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• 2015

In forest work, working conditions are very hard to improve. The good posture is believed to bring about direct improvements such as accident prevention. Therefore, this research carried on analysis of working posture in forest work (construct in stepping-stone) using OWAS analysis system. According to the analytical results provided by OWAS, the ratio of category III (Work posture has a distinctly harmful effect on the musculoskeletal system) has shawn that worker 2 was 32.2%, worker 1 was 25.2% and worker 3 was 15.5%. Furthermore, the ratio of category IV (Work posture with an extremely harmful effect on the musculoskeletal system) has shown that worker 2 was 9.8%, worker 3 was 1.4% and worker 1 was 1.2%. According to the OWAS method, percentage of OWAS action categories III and IV in the worker 2 was higher than another workers.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2011년도 춘계학술대회
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• pp.29-38
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• 2011

• 한국안전학회지
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• 제34권6호
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• pp.29-37
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• 2019

Since construction sites are exposed to outdoor environments, working conditions are significantly dangerous. Thus, wearing of the personal protective equipments such as safety helmet is very important for worker safety. However, construction workers are often wearing-off the helmet as inconvenient and uncomportable. As a result, a small mistake may lead to serious accident. For this, checking of wearing safety helmet is important task to safety managers in field. However, due to the limited time and manpower, the checking can not be executed for every individual worker spread over a large construction site. Therefore, if an automatic checking system is provided, field safety management should be performed more effectively and efficiently. In this study, applicability of deep learning based computer vision technology is investigated for automatic checking of wearing safety helmet in construction sites. Faster R-CNN deep learning algorithm for object detection and classification is employed to develop the automatic checking model. Digital camera images captured in real construction site are used to validate the proposed model. Based on the results, it is concluded that the proposed model may effectively be used for automatic checking of wearing safety helmet in construction site.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.721-727
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• 2022

Construction safety remains an ongoing concern, and project managers have been increasingly forced to cope with myriad uncertainties related to human operations on construction sites and the lack of a skilled workforce in hazardous circumstances. Various construction fatality monitoring systems have been widely proposed as alternatives to overcome these difficulties and to improve safety management performance. In this study, we propose an intelligent, automatic control system that can proactively protect workers using both the analysis of big data of past safety accidents, as well as the real-time detection of worker non-compliance in using personal protective equipment (PPE) on a construction site. These data are obtained using computer vision technology and data analytics, which are integrated and reinforced by lessons learned from the analysis of big data of safety accidents that occurred in the last 10 years. The system offers data-informed recommendations for high-risk workers, and proactively eliminates the possibility of safety accidents. As an illustrative case, we selected a pilot project and applied the proposed system to workers in uncontrolled environments. Decreases in workers PPE non-compliance rates, improvements in variable compliance rates, reductions in severe fatalities through guidelines that are customized according to the worker, and accelerations in safety performance achievements are expected.

• 한국건축시공학회지
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• 제13권6호
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• pp.557-564
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• 2013

건설현장의 불안전하거나 위험한 요소는 안전사고를 유발한다. 안전한 작업환경을 유지하기 위해 위험요소에 대한 경고나 주의 표지를 설치하고 있지만 사소한 부주의 등으로 때때로 사고가 발생한다. 현장에 적재된 자재나 작업을 위해 임시 설치된 가설시설물에 의해서 경고나 주의 표지가 가려지는 경우도 있으며, 작업자가 이동함에 따라 가려진 표지를 파악할 수 없는 위험도 존재한다. 안전사고를 방지하기 위해 GPS나 센서를 이용하여 작업자의 위치추적방법이 연구되고 있지만 본 연구에서는 작업자의 시야를 방해하는 요소를 제거하는 것에 초점을 두고 있다. 본 연구는 적재된 자재로 인해 작업자가 위험요소를 볼 수 없는 문제가 발생할 수 있음을 확인하고, CAD를 이용하여 이를 공사계획과정에서 작업자의 시선에서 관리하는 방법을 제시한다.

• 한국건축시공학회지
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• 제23권5호
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• pp.629-638
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• 2023

본 논문에서는 건설현장 작업자의 실내 위치 추적을 위한 새로운 방법을 소개한다. 전통적으로 GPS및 NTRIP과 같은 기술은 주로 야외에서 효과적인 위치 확인을 제공하는 데 사용되었습니다. 그러나 이러한 기술은 실내에서 사용할 경우 정확도가 떨어지는 문제가 있습니다. 이러한 문제를 해결하기 위해 본 논문에서는 Aruco marker를 활용하여 작업자의 위치를 추적하는 방법을 제안한다. Aruco marker는 작업자와 마커 사이의 거리를 측정하는 데 사용됩니다. 이 새로운 접근 방식은 기존 위치 확인 방법에 비해 더욱 정확한 실내 위치 확인을 제공합니다. 작업자 위치를 실시간으로 확인할 수 있어 작업 일정을 최적화하고 작업자 간 협업을 촉진합니다. 따라서 Aruco marker를 활용한 실내 측위 방식은 기존의 기술의 문제점을 보완하는 실내 위치 확인 시스템으로 활용될 수 있다.

• 대한안전경영과학회지
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• 제16권4호
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• pp.31-40
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• 2014

While Korea had achieved radical growth of construction industry, it also had accumulated problems in material, human and economical loss due to its low quality of safety level. Therefore, not only enterprises but also the nation is putting in a great deal of efforts for construction safety. However, its effect is not satisfiable. This research aims for change of construction cite by introduction of professional Safety Patrol Management System(SPMS) and consideration of its necessity. To consideration of its necessity, we compared and anaylzed average numbers of indicated dangers and safety accident incidences in each construction cites and we researched changes in worker's safety sense. It will establish the suitable design standards and suggest the basic database for estimating disaster and accident ratio.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.983-989
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• 2009

This study aims to systematically identify direct and indirect factors that influence labor productivity and to build a model that mathematically quantifies them so as to efficiently manage and increase labor productivity in the construction work. This study was performed based on the productivity model for workers in reinforced concrete construction projects, because it aims to establish a general construction labor productivity model that reflects many factors that influence labor productivity. Using statistical analysis, we found that the components that significantly influence productivity were the worker component, the work characteristic component, the work technique component, the work management component, the equipment & materials component, and the work guide component, while the work delay components did not significantly influence productivity. In addition, a priority analysis was performed based on the components that showed statistically significant effects. The results of the analysis indicated that the influence of work management component and the work technique component is more than that of the worker component and the work characteristic component. The construction labor productivity model that was formulated in this study could be used for the determining the standard productivity during the initial planning stage, so the best strategy for increasing labor productivity could be formulated.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.443-452
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• 2020

Worker's awkward postures and unreasonable physical load can be corrected by monitoring construction activities, thereby increasing the safety and productivity of construction workers and projects. However, manual identification is time-consuming and contains high human variance. In this regard, an automated activity recognition system based on inertial measurement unit can help in rapidly and precisely collecting motion data. With the acceleration data, the machine learning algorithm will be used to train classifiers for automatically categorizing activities. However, input acceleration data are extracted either from designed experiments or simple construction work in previous studies. Thus, collected data series are discontinuous and activity categories are insufficient for real construction circumstances. This study aims to collect acceleration data during long-term continuous work in a construction project and validate the feasibility of activity recognition algorithm with the continuous motion data. The data collection covers two different workers performing formwork at the same site. An accelerator, as well as portable camera, is attached to the worker during the entire working session for simultaneously recording motion data and working activity. The supervised machine learning-based models are trained to classify activity in hierarchical levels, which reaches a 96.9% testing accuracy of recognizing rest and work and 85.6% testing accuracy of identifying stationary, traveling, and rebar installation actions.

• 건설안전기술
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• 통권53호
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• pp.52-62
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• 2011

Purpose of this study was to comprehend the influence that worker's lob stress caused by the distinct characteristics of construction work affect on construction management. Proven through previous studies of job stress measurement method, physical environment, job demands, job autonomy, interpersonal conflict are derived as typical factors. We analyzed causal relationships between the factors using structural equation modeling under the hypothesis that job stress have effect on the construction management. As a result, successful job stress management for construction management plan is proposed.