• International conference on construction engineering and project management
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• 2015.10a
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• pp.248-251
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• 2015

High injury and fatality rates remain a serious problem in the construction industry. Many construction injuries and fatalities can be prevented if workers can recognize potential hazards and take actions in time. Many efforts have been devoted in improving workers' ability of hazard recognition through various safety training and education methods. However, a reliable approach for evaluating this ability is missing. Previous studies in the field of human behavior and phycology indicate that the visual focus of attention (VFOA) is a good indicator of worker's actual focus. Towards this direction, this study introduces an automated approach for estimating the VFOA of equipment operators using a head orientation-based VFOA estimation method. The proposed method is validated in a virtual reality scenario using an immersive head mounted display. Results show that the proposed method can effectively estimate the VFOA of test subjects in different test scenarios. The findings in this study broaden the knowledge of detecting the visual focus and distraction of construction workers, and envision the future work in improving work's ability of hazard recognition.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.155-163
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• 2008

This paper presents a novel framework for analysis of power system wide-area blackout based on so called fault cascading scenarios. For a given power system operating state, "triggering" faults or a "seed faults" are chosen based on the probabilities estimated from the hazard rates. The fault probabilities reflect both the load and the weather conditions. Effects of hidden failures in protection systems are also reflected in establishing the fault propagation scenarios since they are one of the major causes for the wide-area blackouts. A tree type data structure called a PS-BEST(Power System Blackout Event Scenario Tree) is proposed for construction of the fault cascading scenarios, in which nodes represent various power system operating states and the arcs are the events causing transitions between the states. Arcs can be either probabilistic or deterministic. For a given initial fault, the total probability of leading to wide-area blackout is estimated by aggregating the individual probability of each fault sequence route leading to wide-area blackout. A case study is performed on the IEEE RTS-79(24 bus) system based on the fault data presented by the North American Electrical Reliability Council(NERC). Test results demonstrate the potentials and the effectiveness of the proposed technique for the future wide-area blackout analysis.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.4
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• pp.373-385
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• 2009

A new indexing methodology so called KTH-index was developed to quantitatively evaluate a potential level for tunnel collapse hazard, which has been successfully applied to tunnel construction sites to date. In this study, an attempt is made to apply this methodology for validating an outcome of tunnel design by checking the variation of KTH-index along longitudinal tunnel section. In this KTH-index simulation, it is the most important to determine the input factors reasonably. The design factor and construction condition are set up based on the designed outcome. Uncertain ground conditions are arranged based on borehole test and electro-resistivity survey data. Two scenarios for ground conditions, best and worst scenarios, are set up. From this simulation, it is shown that this methodology could be successfully applied for providing quantitative validity of a tunnel design and also potential hazard factors which should be carefully monitored in construction stage. The hazard factors would affect sensitively the hazard level of the tunnel site under consideration.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1286-1288
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• 2024

The construction industry, known for its inherent risks and multiple hazards, necessitates effective solutions for hazard identification and mitigation [1]. To address this need, the implementation of machine learning models specializing in object detection has become increasingly important because this technological approach plays a crucial role in augmenting worker safety by proactively recognizing potential dangers on construction sites [2], [3]. However, the challenge in training these models lies in obtaining accurately labeled datasets, as conventional methods require labor-intensive labeling or costly measurements [4]. To circumvent these challenges, synthetic data generation (SDG) has emerged as a key method for creating realistic and diverse training scenarios [5], [6]. The paper reviews the evolution of synthetic data generation tools, highlighting the shift from earlier solutions like Synthpop and Data Synthesizer to advanced game engines[7]. Among the various gaming platforms, Unity 3D and Unreal Engine stand out due to their advanced capabilities in replicating realistic construction hazard environments [8], [9]. Comparing Unity 3D and Unreal Engine is crucial for evaluating their effectiveness in SDG, aiding developers in selecting the appropriate platform for their needs. For this purpose, this paper conducts a comparative analysis of both engines assessing their ability to create high-fidelity interactive environments. To thoroughly evaluate the suitability of these engines for generating synthetic data in construction site simulations, the focus relies on graphical realism, developer-friendliness, and user interaction capabilities. This evaluation considers these key aspects as they are essential for replicating realistic construction sites, ensuring both high visual fidelity and ease of use for developers. Firstly, graphical realism is crucial for training ML models to recognize the nuanced nature of construction environments. In this aspect, Unreal Engine stands out with its superior graphics quality compared to Unity 3D which typically considered to have less graphical prowess [10]. Secondly, developer-friendliness is vital for those generating synthetic data. Research indicates that Unity 3D is praised for its user-friendly interface and the use of C# scripting, which is widely used in educational settings, making it a popular choice for those new to game development or synthetic data generation. Whereas Unreal Engine, while offering powerful capabilities in terms of realistic graphics, is often viewed as more complex due to its use of C++ scripting and the blueprint system. While the blueprint system is a visual scripting tool that does not require traditional coding, it can be intricate and may present a steeper learning curve, especially for those without prior experience in game development [11]. Lastly, regarding user interaction capabilities, Unity 3D is known for its intuitive interface and versatility, particularly in VR/AR development for various skill levels. In contrast, Unreal Engine, with its advanced graphics and blueprint scripting, is better suited for creating high-end, immersive experiences [12]. Based on current insights, this comparative analysis underscores the user-friendly interface and adaptability of Unity 3D, featuring a built-in perception package that facilitates automatic labeling for SDG [13]. This functionality enhances accessibility and simplifies the SDG process for users. Conversely, Unreal Engine is distinguished by its advanced graphics and realistic rendering capabilities. It offers plugins like EasySynth (which does not provide automatic labeling) and NDDS for SDG [14], [15]. The development complexity associated with Unreal Engine presents challenges for novice users, whereas the more approachable platform of Unity 3D is advantageous for beginners. This research provides an in-depth review of the latest advancements in SDG, shedding light on potential future research and development directions. The study concludes that the integration of such game engines in ML model training markedly enhances hazard recognition and decision-making skills among construction professionals, thereby significantly advancing data acquisition for machine learning in construction safety monitoring.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.261-263
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• 2008

Determining the most likelihood earthquake scenario in one region is very important for performing an earthquake-resistant design. The most likelihood earthquake scenario can be selected by performing deaggregation, who classifies earthquakes that occur ground motion exceeding a specific acceleration as each distance and each earthquake magnitude. If earthquakes are classified, the most likelihood earthquake scenario can be selected. Earthquake hazard analysis method that have to be performed before deaggregation follows the method that Ministry of Construction & Transportation presented. As a result of performing deaggregation at longitude 127.35 and latitude 34.7, presentable seismic ground motion scenarios can be selected at each recurrence period.

• Earthquakes and Structures
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• v.13 no.4
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• pp.365-375
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• 2017

Seismic risk management has two main technical aspects: to recommend the construction of high-performance buildings and other structures using earthquake-resistant designs or evaluate existing ones, and to prepare emergency plans using realistic seismic scenarios. An overview of seismic risk assessment methodologies in Croatia is provided with details regarding the components of the assessment procedures: hazard, vulnerability and exposure. For Croatia, hazard is presented with two maps and it is expressed in terms of the peak horizontal ground acceleration during an earthquake, with the return period of 95 or 475 years. A standard building typology catalogue for Croatia has not been prepared yet, but a database for the fourth largest city in Croatia is currently in its initial stage. Two methods for earthquake vulnerability assessment are applied and compared. The first is a relatively simple and fast analysis of potential seismic vulnerability proposed by Croatian researchers using damage index (DI) as a numerical value indicating the level of structural damage, while the second is the Macroseismic method.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.415-422
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• 2024

Recent advances in construction automation have led to increased use of deep learning-based computer vision technology for construction monitoring. However, monitoring systems based on supervised learning struggle with recognizing complex risk factors in construction environments, highlighting the need for adaptable solutions. Large multimodal models, pretrained on extensive image-text datasets, present a promising solution with their capability to recognize diverse objects and extract semantic information. This paper proposes a methodology that generates training data for multimodal models, including safety-centric descriptions using GPT-4V, and fine-tunes the LLaVA model using the LoRA method. Experimental results from seven construction site hazard scenarios show that the fine-tuned model accurately assesses safety status in images. These findings underscore the proposed approach's effectiveness in enhancing construction site safety monitoring and illustrate the potential of large multimodal models to tackle domain-specific challenges.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.3
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• pp.278-285
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• 2016

The key features of maritime accidents are the change of their attributes by new risks from time to time. To prevent maritime accidents in Korea, the impacts by new risks on domestic safety environments should be identified or predicted. The purpose of this paper is to find the hazard factors by new risks on maritime safety in Korea. The meaning of new risks is the elements of accident hazard which is compiled from new or rare or unprecedented events in the worldwide maritime transportations. The problems of new risks are the lacks of optimum countermeasures to mitigate accident risks. Using the questionnaires with 152 event scenarios classified by 20 accident causes, the hazard identification and risk analysis of new risks was performed based on the Formal Safety Assessment (FSA) by IMO. A total of 22 Influence Diagrams, which is to depict the transit flows between accident causes to consequences, is used in the construction of 152 event scenarios. A total of 20 accidents causes is the same contents as the causation factors represented in Statistical Year Book for Maritime Accidents of Korean Maritime Safety Tribunals. After defining the evaluation equations to the response results of questionnaires by 46 experts, the work for risk analysis is carried out. As results from the analysis of 152 scenarios, it is known that the root cause to affect on maritime safety in Korea is the pressure of business competition and it led to the lacks of well experienced crews, the overload of vessel operations and crew's fatigue. In addition, as results from the analysis of 20 accident causes, the three accident causes are to be candidate as main issues in Korea such as the inadequate preparedness of departure, the neglecting of watch keeping in bridge and the inadequate management of ship operations. All of the results are thought to be as basic hazard factors to safety impediments. It is thus found that the optimum Risk Control Options to remove the hazard factors and to mitigate consequences required are the following two factors: business competition and crewing problems.

• Journal of the Korean Institute of Gas
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• v.13 no.1
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• pp.34-39
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• 2009

Traditionally all concrete roof type LNG storage tank have been constructing in Korea regardless of LNG tank types. But a steel roof LNG storage tank has merits relatively in designing larger scale tanks and construction cost so it is on the table to apply. This study was carried for the standardized development of impact and fire hazard analysis on a 200,000$k{\ell}$ steel roof LNG storage tank designed by KOGAS and for getting quantitative safety data on a steel roof LNG storage tank compared with a conventional concrete roof LNG storage tank by evaluating with this method. Hazard analysis on each four impact and fire scenarios were developed and evaluated by using finite element methods.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1220-1229
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• 2024

In the construction industry, professionals are required to have advanced problem-solving skills to adeptly handle the dynamic challenges inherent to project execution. These skills are crucial, as they enable professionals to effectively navigate the complexities and unpredictability of construction projects, ensuring timely and cost-effective completion. This paper explores an innovative approach to enhance the problem-solving skills of construction students through the integration of conversational AI-based serious games into their educational curriculum. The objective of this research was acquired by following three phases: hazard interaction, problem identification, and AI-guided text-based communication. This approach creates an engaging learning environment, simulating real-world construction challenges and problems, focusing on the excavation phase of a construction project as a case study for students to interact with and communicate with the Conversational AI agent through text-based prompts. In the future, the proposed study can be used to evaluate how AI agents can help enhance problem-solving skills by promoting emotional engagement among participants. This research sheds light on the potential of integrating conversational AI in education, providing valuable insights for educators designing construction management training programs by underscoring the importance of engagement in real-world problem-solving scenarios.