• 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.

• Journal of Drive and Control
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• v.17 no.4
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• pp.103-112
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• 2020

Field robots are used in various fields, such as agriculture, forestry, manufacturing, and construction; their use has recently expanded to include submarine areas. Field robots can aid in various tasks, such as soil transport, ground clearance, and dismantling of buildings. As field robots are used in a variety of different areas, the difficulty of the work is also quite varied. Increased difficulty is associated with an increased risk of accidents involving the field robot. In order to reduce the accident rate of field robot workers, the need for digitalization and automation of field robots is becoming more of an issue. To this end, it is necessary to study a system that enables workers to do their work without directly contacting a field robot. Therefore, in this paper, we introduce a control system for wireless remote control of a small field robot. The field robot can be wirelessly controlled by a worker in a remote location if the worker cannot be present at the work site. The implemented remote system is tested according to the type of work, and the operating characteristics of the remote system are assessed.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.735-740
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• 2022

The construction is the industry with the highest fatalities, and the fatalities has not decreased despite various institutional improvements. Accordingly, real-time safety management by applying artificial intelligence (AI) to CCTV images is emerging. Although some research on worker detection by applying AI to images of construction sites is being conducted, there are limitations in performance expression due to problems such as complex background due to the nature of the construction industry. In this study, the YOLO model and the OpenPose model were fused to improve the performance of worker detection and posture estimation to improve the detection performance of workers in various complex conditions. This is expected to be highly useful in terms of unsafe behavior and health management of workers in the future.

• Journal of the Korea Institute of Building Construction
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• v.15 no.6
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• pp.621-629
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• 2015

Construction worker safety and safety training continue to be main issues in the construction industry. In order to promote safety awareness among workers, it is imperative to develop a more effective and efficient safety training. This study compared two methods in construction worker safety training: 1) a conventional lecture and 2) 3D simulation through Building Information Modeling. Both training methods included the same contents, a selection of safety standard and guide suggested by Occupational Safety and Health Agency and the Korea Occupational Safety and Health Agency; the contents were then produced into two types of training methods. A survey was conducted targeting on safety managers, in which the managers evaluated lifelikeness, active learning and enjoyment that each of training methods can promote. The results of the survey showed that innovative method using 3D simulation was more effective than conventional lecture method in terms of its lifelikeness, active learning and enjoyment. This study will provide implications that innovative method using the virtual reality is more effective than conventional lecture method.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.269-279
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• 2022

Purpose: The purpose of this study is to develop a deep learning-based personal protective equipment detection model for disaster prevention at construction sites, and to apply it to actual construction sites and to analyze the results. Method: In the method of conducting this study, the dataset on the real environment was constructed and the developed personal protective equipment(PPE) detection model was applied. The PPE detection model mainly consists of worker detection and PPE classification model.The worker detection model uses a deep learning-based algorithm to build a dataset obtained from the actual field to learn and detect workers, and the PPE classification model applies the PPE detection algorithm learned from the worker detection area extracted from the work detection model. For verification of the proposed model, experimental results were derived from data obtained from three construction sites. Results: The application of the PPE recognition model to construction site brings up the problems related to mis-recognition and non-recognition. Conclusions: The analysis outcomes were produced to apply the object recognition technology to a construction site, and the need for follow-up research was suggested through representative cases of worker recognition and non-recognition, and mis-recognition of personal protective equipment.

• Journal of KIBIM
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• v.5 no.2
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• pp.46-54
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• 2015

Vision-based tracking has been proposed as a means to efficiently track a large number of construction resources operating in a congested site. In order to obtain 3D coordinates of an object, it is necessary to employ stereo-vision theories. Detecting and tracking of multiple objects require an entity matching process that finds corresponding pairs of detected entities across the two camera views. This paper proposes an efficient way of entity matching for tracking of construction workers. The proposed method basically uses epipolar geometry which represents the relationship between the two fixed cameras. Each pixel coordinate in a camera view is projected onto the other camera view as an epipolar line. The proposed method finds the matching pair of a worker entity by comparing the proximity of the all detected entities in the other view to the epipolar line. Experimental results demonstrate its suitability for automated entity matching for 3D vision-based tracking of construction workers.

• Fashion & Textile Research Journal
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• v.20 no.6
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• pp.704-712
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• 2018

This study analyzed working postures using the Ovako Working Posture Analysis System (OWAS) to improve work clothes for construction workers. A video taken at a construction work site was stopped at regular intervals and the postures of relevant body parts proposed by OWAS was recorded. Additionally, based on analysis of the working postures code, the level of work action for each postures was classified from stage I to IV. General workers frequently straightened or bent forward at the waist, and used their legs to stand, bend, or walk. Wood workers moved extensively from the waist, keeping their legs relatively straight and their arms held below their shoulders, repeatedly tapping with a hammer weighing less than 10.0kg. Rebar bending workers mainly bent forward at the waist, with both legs bent or standing with one leg bent. Rebar transport and fixing workers walked with the waist straight, and occasionally one or both hands held above the shoulders. Their work also involved holding a hook, which weigh less than 10.0kg, in their hands, and the difficult task of lifting and placing long rebars, which weigh from 10.0 to 20.0kg or more. Concrete pouring workers bent or twisted their back to the side. Therefore, this study suggests that design goals should be different when developing workwear for each type of worker.

• Korean Journal of Construction Engineering and Management
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• v.24 no.5
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• pp.73-82
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• 2023

Optimization of Construction Site Layout Planning (CSLP) heavily relies on workers' travel paths. However, traditional path generation approaches predominantly focus on the shortest path, often neglecting critical variables such as individual wayfinding tendencies, the spatial arrangement of site objects, and potential hazards. These oversights can lead to compromised path simulations, resulting in less reliable site layout plans. While Deep Reinforcement Learning (DRL) has been proposed as a potential alternative to address these issues, it has shown limitations. Despite presenting more realistic travel paths by considering these variables, DRL often struggles with efficiency in complex environments, leading to extended learning times and potential failures. To overcome these challenges, this study introduces a refined model that enhances spatial navigation capabilities and learning performance by integrating workers' visibility into the reward functions. The proposed model demonstrated a 12.47% increase in the pathfinding success rate and notable improvements in the other two performance measures compared to the existing DRL framework. The adoption of this model could greatly enhance the reliability of the results, ultimately improving site operational efficiency and safety management such as by reducing site congestion and accidents. Future research could expand this study by simulating travel paths in dynamic, multi-agent environments that represent different stages of construction.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1187-1193
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• 2022

A construction site is a highly complex and constantly changing environment, where hazardous areas are difficult to detect if workers lack sufficient knowledge and awareness. Thus, frequent worker safety training is required. Numerous studies on using virtual reality (VR) for safety training were published. While they demonstrate the potential for improving the skills necessary to avoid accidents in the construction industry, they remain difficult to apply at actual construction sites. VR requires specialized hardware and software, limiting workers' access and restricting workers' participation in training sessions. As a result, this paper proposes multiple platforms for immersive virtual reality safety training (VRMP) based on Industry Foundation Classes (IFC) and web technologies such as immersive web (WebXR). The VRMP is compatible with mobile and desktop devices currently by workers and demonstrates scenario models familiar to workers. Also, it reduces development time by utilizing Building Information Models (BIM). Additionally, The VRMP collects data from workers in a virtual environment to assess each worker's safety level, assisting workers in effectively and comfortably gaining a better understanding and raising their awareness. This paper develops a case study based on the VRPM in order to assess its effectiveness.