• International conference on construction engineering and project management
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• 2022.06a
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• pp.1202-1208
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• 2022

Techniques based on computer vision are becoming increasingly important in construction safety monitoring. Using AI algorithms can automatically identify conceivable hazards and give feedback to stakeholders. However, the construction site remains various potential hazard situations during the project. Due to the site complexity, many visual devices simultaneously participate in the monitoring process. Therefore, it challenges developing and operating corresponding AI detection algorithms. Safety information resulting from computer vision needs to organize before delivering it to safety managers. This study proposes a framework for computer vision-aided construction safety monitoring using collaborative 4D BIM information to address this issue, called CSM4D. The suggested framework consists of two-module: (1) collaborative BIM information extraction module (CBIE) extracts the spatial-temporal information and potential hazard scenario of a specific activity; through that, Computer Vision-aid Safety Monitoring Module (CVSM) can apply accurate algorithms at the right workplace during the project. The proposed framework is expected to aid safety monitoring using computer vision and 4D BIM.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.36
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• pp.175-182
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• 1995

Industrial safety management program can be divided three part that is education, technology, and management. The effectiveness of a industrial safety management program depends on the ability to manage hardware which is technology and software, education and management, In this research, it will be described that how to design and develop Computer Integrated Safety System and Computer Based Training System for Press operations which is how to integrated industrial safety program wi th production planning and control in order to control efficiently using personnel computer system.

• Journal of Construction Engineering and Project Management
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• v.9 no.4
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• pp.16-33
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• 2019

The increasing size and complexity of modern construction projects demands mature capabilities of onsite personnel with regard to recognizing unsafe situations. Construction safety training is paper or computer-based and suffers from a distinct gap between the classroom training environment and real-world construction sites; even trained personnel can find it difficult to recognize many of the potential safety hazards at their jobsites even after receiving construction safety training. Immersive technologies can overcome the current limitations in construction safety training by reducing the gap between the classroom and a real construction environment. This research developed and tested a new Augmented Reality (AR)-based assessment tool to evaluate the hazard recognition skills of students majoring in construction management as part of a construction safety course. The quantitative and qualitative results of this research confirmed that AR-based assessment can become a very effective assessment tool to evaluate safety knowledge and skills in a construction safety course, outperforming both paper and computer-based assessment methods. The students preferred AR-based assessment because it provides a realistic visual context for real world safety hazards.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.87-95
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• 2020

Generally, occupational safety and particularly construction safety is an intricate phenomenon. Industry professionals have devoted vital attention to enforcing Occupational Safety and Health (OHS) from the last three decades to enhance safety management in construction. Despite the efforts of the safety professionals and government agencies, current safety management still relies on manual inspections which are infrequent, time-consuming and prone to error. Extensive research has been carried out to deal with high fatality rates confronting by the construction industry. Sensor systems, visualization-based technologies, and tracking techniques have been deployed by researchers in the last decade. Recently in the construction industry, computer vision has attracted significant attention worldwide. However, the literature revealed the narrow scope of the computer vision technology for safety management, hence, broad scope research for safety monitoring is desired to attain a complete automatic job site monitoring. With this regard, the development of a broader scope computer vision-based risk recognition system for correlation detection between the construction entities is inevitable. For this purpose, a detailed analysis has been conducted and related rules which depict the correlations (positive and negative) between the construction entities were extracted. Deep learning supported Mask R-CNN algorithm is applied to train the model. As proof of concept, a prototype is developed based on real scenarios. The proposed approach is expected to enhance the effectiveness of safety inspection and reduce the encountered burden on safety managers. It is anticipated that this approach may enable a reduction in injuries and fatalities by implementing the exact relevant safety rules and will contribute to enhance the overall safety management and monitoring performance.

• Journal of the Korea Safety Management & Science
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• v.10 no.1
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• pp.1-10
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• 2008

With the development of the Korean economy, the number of arson fire has been radically increased and become a huge problem and issue in Korea Society for last several decades. This study is to establish the fire life safety strategy regarding the arson fire through researching domestic fire statistics and performing the computer simulation based on fire scenarios with cutting edge techniques and methods for fire characteristics and fire dynamic. In addition, to design the fire life safety strategy depending on the arson fire pattern, the flow and characteristics of fire flames and smoke are analyzed by the computer modeling.

• 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.

• Proceedings of the Safety Management and Science Conference
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• 2007.11a
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• pp.239-253
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• 2007

With the development of the Korean economy, the number of arson fire has been radically increased and become a huge problem and issue in Korea Society for last several decades. This study is to establish the fire life safety strategy regarding the arson fire through performing the computer simulation based on fire scenarios with researching domestic fire statistics and cutting edge techniques and methods for fire characteristics and fire dynamic. In addition, to design the fire life safety strategy depending on the arson fire pattern, the flow and characteristics of fire flames and smoke is analyzed by the computer modeling.

• Proceedings of the Safety Management and Science Conference
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• 2011.11a
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• pp.583-591
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• 2011

A number of Brain-Computer Interface (BCI) studies have been performed to assess the cognitive status through EEG signal. However, there are a few studies trying to prevent user from unexpected safety-accident in BCI study. The EEGs were collected from 19 subjects who participated in two experiments (rest & event-related potential measurement). There was significant difference in EEG changes of both spontaneous and event-related potential. Beta power and P300 latency may be useful as a biomarker for prevention of response to safety-accident.

• Journal of the Korea Safety Management & Science
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• v.14 no.1
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• pp.1-6
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• 2012

A number of Brain-Computer Interface (BCI) studies have been performed to assess the cognitive status through EEG signal. However, there are a few studies trying to prevent user from unexpected safety-accident in BCI study. The EEGs were collected from 19 subjects who participated in two experiments (rest & event-related potential measurement). There was significant difference in EEG changes of both spontaneous and event-related potential. Beta power and P300 latency may be useful as a biomarker for prevention of response to safety-accident.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.344-353
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• 2008

To apply the appropriate new grounding system to domestic power system, safety has to be guaranteed under the given circumstances. It is not possible to decide the safety of grounding systems by the experimental test because safety experiments directly relate to the human life and the installed electric machines. Therefore, the computer simulation program to decide the safety of grounding systems based on the IEC standard systems, has to be developed. This paper proposes the computer simulation based method to decide the safety of grounding system with the concepts of touch voltage, step voltage, human resistivity, and applied electric current according to the several conditions of human body located in the corresponding grounding systems. The proposed method is implemented by Matlab/Simulink and Visual C++ programming tools for its visualization.