• Korean Journal of Agricultural Science
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• v.42 no.2
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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.

• Proceedings of the Safety Management and Science Conference
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• 2011.04a
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• pp.29-38
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• 2011

• Journal of the Korean Society of Safety
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• v.34 no.6
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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.

• International conference on construction engineering and project management
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• 2022.06a
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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.

• Journal of the Korea Institute of Building Construction
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• v.13 no.6
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• pp.557-564
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• 2013

Unsafe and dangerous factors or environments on building construction sites often cause safety accidents. Sometimes, accidents happen despite the existence of warning and caution signs. Such warning signs are likely to be hidden by stacked materials or temporary facilities on site. If workers cannot see the signs when moving or working, they could be injured, sometimes fatally. Many studies have focused on each worker's position, and various systems have been developed using GPS signals and sensors. This paper suggests a method for identifying the problem of worker inability to see the unsafe factors, to manage the problem in the construction planning phase by considering things from the worker's perspective. The method uses CAD software to investigate the relation between the height of stacked materials and the visible ratio of the unsafe factor. The results of a sample project show that the changes in the height and location of stacked materials make the site safer.

• Journal of the Korea Institute of Building Construction
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• v.23 no.5
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• pp.629-638
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• 2023

This study introduces an innovative approach to monitor the whereabouts of workers within indoor construction settings. While traditional modalities such as GPS and NTRIP have demonstrated efficacy for outdoor localizations, their precision dwindles in indoor environments. In response, this research advocates for the adoption of Aruco markers. Leveraging computer vision technology, these markers facilitate the quantification of the distance between a worker and the marker, subsequently pinpointing the worker's instantaneous location with heightened accuracy. The methodology's efficacy was rigorously evaluated in a real-world construction scenario. Parameters including system stability, the influence of lighting conditions, the extremity of measurable distances, and the breadth of recognition angles were methodically appraised. System stability was ascertained by maneuvering the camera at a uniform velocity, gauging its marker recognition prowess. The impact of varying luminosity on marker discernibility was scrutinized by modulating the ambient lighting. Furthermore, the camera's spatial movement ascertained both the upper threshold of distance until marker recognition waned and the maximal angle at which markers remained discernible.

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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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.

• International conference on construction engineering and project management
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• 2009.05a
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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.

• International conference on construction engineering and project management
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• 2020.12a
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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.

• Journal of the Korea Construction Safety Engineering Association
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• s.53
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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.