Most factories deal with toxic or flammable chemicals in their industrial processes. These hazardous substances pose a risk of leakage due to accidents, such as fire and explosion. In the event of chemical release, massive casualties and property damage can result; hence, quantitative risk prediction and assessment are necessary. Several methods are available for evaluating chemical dispersion in the atmosphere, and most analyses are considered neutral in dispersion models and under far-field wind condition. The foregoing assumption renders a model valid only after a considerable time has elapsed from the moment chemicals are released or dispersed from a source. Hence, an initial dispersion model is required to assess risk quantitatively and predict the extent of damage because the most dangerous locations are those near a leak source. In this study, the dispersion model for initial consequence analysis was developed with three-dimensional unsteady advective diffusion equation. In this expression, instantaneous leakage is assumed as a puff, and wind velocity is considered as a coordinate transform in the solution. To minimize the buoyant force, ethane is used as leaked fuel, and two different diffusion coefficients are introduced. The calculated concentration field with a molecular diffusion coefficient shows a moving circular iso-line in the horizontal plane. The maximum concentration decreases as time progresses and distance increases. In the case of using a coefficient for turbulent diffusion, the dispersion along the wind velocity direction is enhanced, and an elliptic iso-contour line is found. The result yielded by a widely used commercial program, ALOHA, was compared with the end point of the lower explosion limit. In the future, we plan to build a more accurate and general initial risk assessment model by considering the turbulence diffusion and buoyancy effect on dispersion.

Currently, in Korea, lower and upper tier qualifying quantities of the 97 substances requiring preparation for accidents have been designated. The information on the submission of chemical accident prevention management plan varies depending on whether the handling volume is above or below the lower or upper qualifying quantity. Because the criteria of the lower and upper qualifying quantities of substance requiring preparation for accidents are not stipulated in the Chemical Substances Control Act, this study attempted to establish a criterion through significance verification. In addition, the study investigated whether these qualifying quantities are related to the Globally Harmonized System of Classification and Labelling of Chemicals (GHS), toxic concentration endpoint, and National Fire Protection Association (NFPA). Finally, by comparing the risk categorization of the GHS, endpoint, and NFPA, it was evaluated whether the circulation-volume-based risk categorization of the substance requiring preparation for accidents that are in the top 13 is appropriate. The qualifying quantities of benzene, toluene, and sulfuric acid needed to be adjusted upward, while those of methyl alcohol and ammonia were adjusted downward from the current qualifying quantities. It is required to establish a quantified criterion that fully reflects the domestic situations in Korea and various indicators such as toxicity, physicochemical properties, and circulation volume for the qualified criterion of hazardous chemical substances. The study is expected to be helpful in establishing an efficient system by systematizing the criterion for qualifying quantity.

Construction accidents are difficult to prevent because several different types of activities occur simultaneously. The current method of accident analysis only indicates the number of occurrences for one or two variables and accidents have not reduced as a result of safety measures that focus solely on individual variables. Even if accident data is analyzed to establish appropriate safety measures, it is difficult to derive significant results due to a large number of data variables, elements, and qualitative records. In this study, in order to simplify the analysis and approach this complex problem logically, data preprocessing techniques, such as latent class cluster analysis (LCCA) and predictor importance were used to discover the most influential variables. Finally, the correlation was analyzed using an alluvial flow diagram consisting of seven variables and fourteen elements based on accident data. The alluvial diagram analysis using reduced variables and elements enabled the identification of accident trends into four categories. The findings of this study demonstrate that complex and diverse construction accident data can yield relevant analysis results, assisting in the prevention of accidents.

This study analyzes the impact that accidents and near misses have on clients' and contractors' awareness of safety culture. Due to the unique characteristic of employment structure in Korea, the occurrence of accidents differs by company size, which has relevant implications for the establishment of safety culture. Attention has been drawn to the importance of the management of accidents and near misses, with safety awareness acting as a core factor. A positive effect on the prevention of accidents could be achieved by noting the difference in safety awareness between clients and contractors and suggesting an associated suitable safety management system. In support of this study, a survey was distributed to workers in the automobile manufacturing industry (May-August 2020), and data from a total of 574 workers was collected and analyzed, including 399 clients' worksers and 175 contractors' workers. The questionnaire addressed participants' experiences of accidents and near misses as well as 50 items from the Nordic Occupational Safety Climate Questionnaire. Analysis of the responses was conducted using the methods of frequency analysis, Fisher's exact test, t-test, correlation analysis, and regression analysis. The results demonstrated that clients had more experiences with accidents and near misses compared to contractors. Additional differences between clients and contractors were noted in terms of the safety culture factors of learning, communication, and trust. A correlation was observed between the experience of accidents and safety justice management: for clients and contractors who experienced accidents, safety justice management was 9.4 times higher. Furthermore, clients' and contractors' awareness of employees' commitment to safety was determined to be 28.5 times higher in those who had experienced near misses This study concludes that, in order to improve accident prevention through the management of accidents and near misses, clients must focus on overseeing safety justice management and aspects of safety culture factors, while contractors must focus efforts on managing employees' commitment to safety. In further applications, this study could provide baseline data for health and safety activities in terms of the safety culture of clients and contractors. Further study on the establishment of safety culture as related to employment structure is proposed for future research.

When an accident occurs, the associated human activity is typically regarded as a "human error," or a temporal deviation. On the other hand, if the accident results in a serious loss or if it evokes a social issue, the person determined to be responsible may be punished with a "violation" of related laws or regulations. However, as Heinrich stated, it is neither appropriate nor reasonable in terms of probability theory and cognitive science to distinguish whether it is a "human error" or a "violation" with a criterion of resultant accident severity. Nonetheless, some in society get on the social climate to strengthen regulations on workers who have caused accidents, especially violations. This response can present a social issue due to the lack of systematic judgment procedure which distinguishes violations from human errors. The purpose of this study was to develop an objective and systematic procedure to assess whether workers' activities which induced industrial accidents should be categorized as violations rather than human errors. Various analysis techniques for the determination of violation procedure were investigated and compared using an analysis approach method. An appropriate technique was not found, however, for judging the culpability of intentional violations. As an alternative, this study developed the process of creating violations, based on cognitive procedure, as well as the criteria to determine and categorize an activity as a violation. In addition, the developed procedure was applied to cases of industrial accidents and nuclear power plant issues to test its practical applicability. The study demonstrated that the proposed model could be used to determine the existence of a violation even in the case of multiple workers who work simultaneously.

This paper describes an attempt to improve and optimize the operational safety level of a domestic research reactor by conducting a probabilistic safety assessment (PSA) under full-power operating conditions. The PSA was undertaken to assess the level of safety at an operating research reactor in Korea, to evaluate whether it is probabilistically safe and reliable to operate, and to obtain insights regarding the requisite procedural and design improvements for achieving safer operation. The technical objectives were to use the PSA to identify the accident sequences leading to core damage, and to conduct sensitivity analyses based thereon to derive insights regarding potential design and procedural improvements. Based on the dominant accident sequences identified by the PSA, eight types of sensitivity analysis were performed, and relevant insights for achieving safer operation were derived. When these insights were applied to the reactor design and operating procedure, the risk was found to be reduced by approximately ten times, and the safety was significantly improved. The results demonstrate that the PSA methodology is very effective for improving reactor safety in the full-power operating phase. In particular, it is a highly suitable approach for identifying the deficiencies of a reactor operating at full power, and for improving the reactor safety by overcoming those deficiencies.

The purpose of this study is to provide implications for improving the safety behavior of university lab students by grasping the relationship between leaders' safety leadership, the lab's safety climate, and individual safety motivation variables. To this end, a survey was conducted on nationwide university laboratories, and the main results are as follows. First, transformational and transactional safety leadership had a positive effect on safety behavior, while the laissez-faire one had a negative impact.Second, it was found that the laboratory safety climate plays a mediating role in the relationship in which safety leadership affects safety behavior. Third, it was found that individual safety motivation was governed by the laboratory safety climate's effect on safety behavior. Fourth, individual safety motivation was found to control the mediating effect of the laboratory safety climate in the relationship between safety leadership and safety behavior. The conclusion of this study is the following: to strengthen the safety behavior of university laboratory students, the laboratory manager must operate the laboratory with a transformational and transactional safety leadership. Additionally, educational institutions must effectively operate their own safety-day programs to create a safe climate for each laboratory and improve students' safety motives.

Fire-alarm systems are safety equipment that facilitate rapid evacuation and early suppression in case of fire. It is highly desirable that fire-alarm systems have low false-alarm rates and are thus reliable. Until now, researchers have attempted to improve detector performance by applying new technologies such as IoT. To this end, IoT-based fire-detection systems have been developed. However, due to scarcity of large-scale operational data, researchers have barely studied malfunctioning in fire-alarm systems or attempted to reduce false-alarm rates in these systems. In this study, we analyzed false-alarm rates of smoke/temperature detectors and unwanted fire-alarm signal patterns at K institution, where Korea's largest IoT-based fire-detection system operates. After analyzing the fire alarm occurrences at the institution for five years, we inferred that the IoT-based fire-detection system showed lower false-alarm rates compared to the automatic fire-detection equipment. We analyzed the detection pattern by dividing it into two parts: normal operation and unwanted fire alarms. When a specific signal pattern was filtered out, the false-alarm rate was reduced to 66.9% in the smoke detector and to 46.9% in the temperature detector.

Lithium-ion batteries (LIBs) have attracted much interest for their high energy density (>150 mAh/g), high capacity, low self-discharge rate, and high coulombic efficiency. However, with the successful commercialization of LIBs, fire and explosion incidents are likely to increase. The thermal runaway is known as the major factor in battery-related accidents that can lead to a series of critical conditions. Considering this, recent studies have shown an increased interest in countering the safety issues associated with LIBs. Although safety standards for LIB use have recently been formulated, little attention has been paid to the safety around the manufacturing process for battery products. The present study introduces a risk assessment method suitable for assessing the safety of the LIB-manufacturing process. In the assessment method, a compensation parameter (Z-factor) is employed to correctly evaluate the process's safety on the basis of the type of material (e.g., metal anode, liquid electrolyte, solid-state electrolytes) utilized in a cell. The proposed method has been applied to an 18650 cell-manufacturing process, and three sub-processes have been identified as possibly vulnerable parts (risk index: >4). This study offers some crucial insights into the establishment of safety standards for battery-manufacturing processes.

Widespread infectious diseases are a concern for workers working in confined spaces. However, there is no risk assessment index for the risk of infectious disease in the workplace. Therefore, we propose a simple but effective index model to assess the risk of infectious diseases in the workplace. The proposed model identifies the risk of each workplace through an evaluation sheet comprising the frequency and intensity of the infectious disease. The intensity of an infectious disease is generally governed by the density of workers, whereas frequency is the sum of physical-e nvironmental and human management factors. By multiplying intensity and frequency, the risk of the workplace is derived. Through the proposed model, we evaluate the risks of workers at 15 different work sites. The proposed model clearly reveals what should be improved to keep workers safe from infectious diseases and will be helpful in assessing the risk of contagious disease at the work place.

Human casualties from fires are increasing worldwide. The majority of human deaths occur during the evacuation process, as occupants panic and are unaware of the location of the fire and evacuation routes. Using an Internet of Things (IoT) sensor and reinforcement learning, we propose a method to find the safest evacuation route by considering the fire location, flame speed, occupant position, and walking conditions. The first step is detecting the fire with IoT-based devices. The second step is identifying the occupant's position via a beacon connected to the occupant's mobile phone. In the third step, the collected information, flame speed, and walking conditions are input into the reinforcement learning model to derive the optimal evacuation route. This study makes it possible to provide the safest evacuation route for individual occupants in real time. This study is expected to reduce human casualties caused by fires.