• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.498-509
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• 2023

Purpose: This paper proposes a method detecting the falling of a maintenance worker in the underground utility tunnel, by applying deep learning techniques using CCTV video, and evaluates the applicability of the proposed method to the worker monitoring of the utility tunnel. Method: Each rule was designed to detect the falling of a maintenance worker by using the inference results from pre-trained YOLOv5 and OpenPose models, respectively. The rules were then integrally applied to detect worker falls within the utility tunnel. Result: Although the worker presence and falling were detected by the proposed model, the inference results were dependent on both the distance between the worker and CCTV and the falling direction of the worker. Additionally, the falling detection system using YOLOv5 shows superior performance, due to its lower dependence on distance and fall direction, compared to the OpenPose-based. Consequently, results from the fall detection using the integrated dual deep learning model were dependent on the YOLOv5 detection performance. Conclusion: The proposed hybrid model shows detecting an abnormal worker in the utility tunnel but the improvement of the model was meaningless compared to the single model based YOLOv5 due to severe differences in detection performance between each deep learning model

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.205-206
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• 2023

This paper presents a new method for indoor localization track workers in construction sites. While GPS and NTRIP are effective for outdoor positioning, they are less accurate when used indoors. To address this issue, the proposed method utilizes Aruco markers to measure the distance between workers and the markers. By collecting data values, the location of each worker can be determined in real-time with high accuracy. This approach has the potential to enhance work efficiency and safety at construction sites, as it provides more precise indoor positioning compared to conventional methods, leading to improved work efficiency.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.602-603
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• 2015

Despite all efforts to reduce construction disaster, construction site accident rate has steadily increased in Korea since 2008. As a different approach from traditional research, there is a growing issue about design for safety concept to prevent construction disaster. The notion is that construction worker's safety need to be considered at design phase, not only at construction phase. Globally, the notion has been noted that to improve the safety of the worker and used in practice. However, in Korea, most of safety management guidelines are limited to construction phase. From recent statistics, only 1.4 percent of designer feel responsible for safety accident at construction site. In this circumstance, this research find out safety guidelines through literatures reviews and practical experience of safety management in other country which apply design for safety concept. Selected guidelines are verified by survey which is evaluated with risk, function, cost, time and aesthetic view categories. Through the survey, define guidelines which could be effectively applied in Korea. By using proposed safety guidelines for design phase, preventing construction accident and improving designer's recognition of safety issue at design phase are expected.

• Korean Journal of Construction Engineering and Management
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• v.21 no.3
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• pp.65-75
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• 2020

In this study, the purpose of the study was to verify the relationship between the company's construction safety management system and workers' safety consciousness and safety observance behavior. A survey of 357 workers was conducted and a statistical analysis was performed using the SPSS 25.0 program. As a result, the following main results were obtained: First, it was found that supervisor's capability, inner construction safety management, construction safety education, and construction disaster evaluation have significant effects on the worker's safety consciousness. Second, it was revealed that the worker's safety consciousness had a significant positive effect on the safety observance behavior. Third, it was shown that supervisor's capability, inner construction safety management, construction safety education, and construction disaster evaluation have significant effects on safety observance behavior. In addition, this study confirmed that the management supervisor's ability to handle safety at the work site is the most important factor in safety management in order to enhance the worker's safety consciousness and safety observance behavior.

• Journal of the Korea Institute of Building Construction
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• v.23 no.1
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• pp.81-92
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• 2023

According to the 2020 industrial accident reports of the Ministry of Employment and Labor, the number of fatal accidents in the construction industry over the past 5 years has been higher than in other industries. Of these more than 50% of fatal accidents are initially caused by fall accidents. The central government is intensively managing falling/jamming protection device and the use of personal protective equipment to eradicate the inappropriate factors disrupting safety at construction sites. In addition, although efforts have been made to prevent safety accidents with the proposal of the Special Act on Construction Safety, fatalities on construction sites are constantly occurring. Therefore, this study developed a model that automatically detects the wearing state of the worker's safety helmet and belt using computer vision technology. In considerations of conditions occurring at construction sites, we suggest an optimization method, which has been verified in terms of the accuracy and operation speed of the proposed model. As a result, it is possible to improve the efficiency of inspection and patrol by construction site managers, which is expected to contribute to reinforcing competency of safety management.

• Journal of the Korean GEO-environmental Society
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• v.14 no.8
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• pp.45-51
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• 2013

In the construction site of underground buildings which have severe environment such as dust, noise, vibration, the technology of rescue the builders in the construction site when accident occurs by tracking the location of the builders and express the mission of supervisor smoothly. In this study, in order to acquire the location information of the builders in the construction site of underground buildings by using MEMS INS and air pressure sensor, we firstly performed the field test in construction site, analyzed the location and the elevation accuracy based on the detected results, and then verified its practicality and rationality after all. As a result, we could acquire worker's position-accuracy within 10m in horizontal direction and 4m in vertical direction. Therefore we could judge availability in construction fields of underground structure.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1242-1242
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• 2022

Annually, several construction workers fall ill, are injured, or die due to heat-related exposure. The prevalence of work-related heat illness may rise and become an issue for workers operating in temperate climates, given the increase in frequency and intensity of heatwaves in the US. An increase in temperature negatively impacts physical exertion levels and mental state, thereby increasing the potential of accidents on the job site. To reduce the impact of heat stress on workers, it is critical to develop and implement measures for monitoring physical exertion levels and mental state in hot conditions. For this, limited studies have evaluated the utility of wearable biosensors in measuring physical exertion and mental workload in hot conditions. In addition, most studies focus solely on male participants, with little to no reference to female workers who may be exposed to greater heat stress risk. Therefore, this study aims to develop a process for objective and continuous assessment of worker physical exertion and mental workload using wearable biosensors. Physiological data were collected from eight (four male and four female) participants performing a simulated drilling task at 92oF and about 50% humidity level. After removing signal artifacts from the data using multiple filtering processes, the data was compared to a perceived muscle exertion scale and mental workload scale. Results indicate that biosensors' features can effectively detect the change in worker physical and mental state in hot conditions. Therefore, wearable biosensors provide a feasible and effective opportunity to continuously assess worker physical exertion and mental workload.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.877-885
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• 2022

Vision-based activity recognition has been widely attempted at construction sites to estimate productivity and enhance workers' health and safety. Previous studies have focused on extracting an individual worker's postural information from sequential image frames for activity recognition. However, various trades of workers perform different tasks with similar postural patterns, which degrades the performance of activity recognition based on postural information. To this end, this research exploited a concept of human-object interaction, the interaction between a worker and their surrounding objects, considering the fact that trade workers interact with a specific object (e.g., working tools or construction materials) relevant to their trades. This research developed an approach to understand the context from sequential image frames based on four features: posture, object, spatial features, and temporal feature. Both posture and object features were used to analyze the interaction between the worker and the target object, and the other two features were used to detect movements from the entire region of image frames in both temporal and spatial domains. The developed approach used convolutional neural networks (CNN) for feature extractors and activity classifiers and long short-term memory (LSTM) was also used as an activity classifier. The developed approach provided an average accuracy of 85.96% for classifying 12 target construction tasks performed by two trades of workers, which was higher than two benchmark models. This experimental result indicated that integrating a concept of the human-object interaction offers great benefits in activity recognition when various trade workers coexist in a scene.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.904-911
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• 2022

Occlusion is one of the most challenging problems for computer vision-based construction monitoring. Due to the intrinsic dynamics of construction scenes, vision-based technologies inevitably suffer from occlusions. Previous researchers have proposed the occlusion handling methods by leveraging the prior information from the sequential images. However, these methods cannot be employed for construction object detection in non-sequential images. As an alternative occlusion handling method, this study proposes a data augmentation-based framework that can enhance the detection performance under occlusions. The proposed approach is specially designed for rebar occlusions, the distinctive type of occlusions frequently happen during construction worker detection. In the proposed method, the artificial rebars are synthetically generated to emulate possible rebar occlusions in construction sites. In this regard, the proposed method enables the model to train a variety of occluded images, thereby improving the detection performance without requiring sequential information. The effectiveness of the proposed method is validated by showing that the proposed method outperforms the baseline model without augmentation. The outcomes demonstrate the great potential of the data augmentation techniques for occlusion handling that can be readily applied to typical object detectors without changing their model architecture.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.450-457
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• 2022

The construction industry is demanding, dynamic, and complex making it difficult for workers to recognize hazards. The nature of construction tasks exposes workers to several critical risk factors, such as a high rate of exertion and fatigue. Recent studies suggest that fatigue may impact hazard recognition in the construction industry. However, most studies rely on subjective measures when assessing the relationship between physical fatigue and hazard recognition, limiting such studies' efficacy. Thus, this study examined the relationship between physical fatigue and hazard recognition using a controlled experiment. Worker fatigue levels were captured using physiological data and a subjective exertion scale. The findings confirmed that physical exertion plays a significant role in hazard recognition skills (p < 0.05). This research contributes to theory and practice by providing a process for objectively assessing the influence of physical fatigue on worker safety and providing construction professionals with some critical insight needed to improve workplace safety.