To simulate a process model in the field of chemical engineering, it is very important to identify the physical properties of novel materials as well as existing materials. However, it is difficult to measure the physical properties throughout a set of experiments due to the potential risk and cost. To address this, this study aims to develop a property prediction model based on the group contribution method for aromatic chemical compounds including benzene rings. The benzene rings of aromatic materials have a significant impact on their physical properties. To establish the prediction model, 42 important functional groups that determine the physical properties are considered, and the total numbers of functional groups on 147 aromatic chemical compounds are counted to prepare a dataset. Support vector regression is employed to prepare a prediction model to handle sparse and high-dimensional data. To verify the efficacy of this study, the results of this study are compared with those of previous studies. Despite the different datasets in the previous studies, the comparison indicated the enhanced performance in this study. Moreover, there are few reports on predicting the physical properties of aromatic compounds. This study can provide an effective method to estimate the physical properties of unknown chemical compounds and contribute toward reducing the experimental efforts for measuring physical properties.

This study analyses the load imbalance of the tower crane used in telescoping work for structural safety, owing to the difference in wind speed and balance weight position. This is because wind speed and position of the balance weight have a significant impact on the structural stresses of a tower crane during telescoping work. Therefore, structural analysis was performed on the 290HC model, which is often used at construction sites and has only one cylinder installed. Moreover, two models were classified to determine the load acting on the connecting part of the telescopic cage to slewing platform and the cylinder. Five types of balance weight positions were applied at regular intervals from jibs; moreover, four types of wind load criteria were differently applied. Hence, the telescopic cage columns were destroyed at all balance weight positions at a wind speed of 30 m/s and only at certain locations at a wind speed of 20 m/s. Furthermore, failures occurred for cylinders, torsional, and bending at wind speeds of 30 m/s and 20 m/s, load imbalances above the allowable thresholds considering the safety factor. In addition, the load imbalance in the telescoping work also varied depending on the position of the balance weights. The results of these studies have validated that the current standards of adjusting the appropriate position of the balance weights on the jib are completely valid, with the telescoping work to be executed only at wind speeds of less than equal to 10 m/s.

In many developed countries, such as South Korea, efficiently maintaining the aging infrastructures is an important issue. Currently, inspectors visually inspect the infrastructure for maintenance needs, but this method is inefficient due to its high costs, long logistic times, and hazards to the inspectors. Thus, in this paper, a novel crack inspection approach for concrete bridges is proposed using integrated image processing techniques. The proposed approach consists of four steps: (1) training a deep learning model to automatically detect cracks on concrete bridges, (2) acquiring in-situ images using a drone, (3) generating orthomosaic images based on 3D modeling, and (4) detecting cracks on the orthmosaic image using the trained deep learning model. Cascade Mask R-CNN, a state-of-the-art instance segmentation deep learning model, was trained with 3235 crack images that included 2415 hard negative images. We selected the Tancheon overpass, located in Seoul, South Korea, as a testbed for the proposed approach, and we captured images of pier 34-37 and slab 34-36 using a commercial drone. Agisoft Metashape was utilized as a 3D model generation program to generate an orthomosaic of the captured images. We applied the proposed approach to four orthomosaic images that displayed the front, back, left, and right sides of pier 37. Using pixel-level precision referencing visual inspection of the captured images, we evaluated the trained Cascade Mask R-CNN's crack detection performance. At the coping of the front side of pier 37, the model obtained its best precision: 94.34%. It achieved an average precision of 72.93% for the orthomosaics of the four sides of the pier. The test results show that this proposed approach for crack detection can be a suitable alternative to the conventional visual inspection method.

Governmental bodies generally conduct investigations for product safety management once new products are released to the market, in order to prevent distribution of illegal and defective items. Further, market surveillance activities are regularly conducted by the government to ensure distribution of safe products and recall any hazardous products. The safety investigations often involve a consumer monitoring system wherein consumer organizations participate in market monitoring to conduct surveys on illegal and defective products. As a result, the monitoring results of individual consumer organizations are available separately, but an integrated analysis of the data from all consumer organizations cannot be performed, thereby deterring comprehensive evaluation of the consumer monitoring system. In this study, we analyze the individual monitoring results of consumer organizations to understand the overall status and performance of comprehensive market monitoring and present the directions for desirable market surveillance policies. To this end, the effectiveness of market surveillance related to the distribution of products is verified through analysis of the interrelationships between the monitoring processes and performances of consumer organizations as well as their impact on the performances of the monitoring implementation processes; moreover, several improvement points and direction points are presented for more desirable monitoring of the consumer markets.

Effective leadership in safety performance, such as safety leadership, has received attention as a factor that greatly affects safety performance in various high-risk industrial environments. Based on prior research, this study aims to analyze the impact relationship between safety leadership, safety observance, safety education, and safety culture to reveal the effectiveness of safety leadership. In addition, this study will examine the effects of safety leadership on safety observance and safety education to identify the effects of safety understanding. For this purpose, a survey was conducted with employees of an aviation manufacturing company in Gyeongnam. For verification, a positive factor analysis, correlation analysis, and structural equation analysis were conducted using the AMOS 21.0 program. This study's findings show that, first, safety leadership has a positive and significant impact on safety observance and safety education. Second, safety leadership was found to have a direct impact on organizations' safety culture. Third, although safety observance has significant positive effects on safety culture, safety education has often been rejected. Finally, after verifying the control effect of safety understanding, this study uncovered the presence of an adjustment effect in the effect of safety leadership on safety observance. Therefore, based on the results of this study, theoretical and practical implications suggest that through the exercise of safety leadership, managers can improve their organizations' safety culture by increasing organizations' compliance with safety observance and/or acceptance of safety education.

Domestic industrial accidents continue to increase, with 2,142 deaths in 2018, up by 185 (9.5%) from 1,957 deaths in 2017. Industrial accidents that cause loss of human lives pose a serious risk to businesses because of the strengthening of safety regulations and the changing public perception of social responsibility. Accordingly, to prevent industrial accidents, companies regularly conduct onsite safety activities and conduct education and training to raise awareness among employees. However, many such corporate activities are not conducted voluntarily and practically by employees but mostly by formal implementation. To discontinue this customary and passive behavior of employees and establish a mature safety culture, strengthening the execution power of safety management at the site is of paramount importance, and to this end, we aim to utilize the safety practice index (SPI). In this study, the SPI calculated on the basis of the results of the 2018 and 2019 risk management and safety activities of a site was compared with the reported safety accidents. The results confirmed that the SPI index can be used as a valid indicator for safety activities for accident prevention, such as strengthening leadership and safety policies to grade and manage safety management levels for a certain period of time or by a department or to convert weaknesses into strengths.

This study aims to analyze an influence of industrial accident on industrial productivity. We analyzed relationship among industrial accident, labor force, and industrial productivity using vector error correction model (VECM). The data used in the analysis were the number of industrial accidents, the number of workers, and index of all industry production from January 2008 to June 2017 in Korea. Finally, the industrial accidents have played a role in reducing labor force and industrial productivity.

The narrative texts of industrial accident reports help to identify accident risk factors. They relate the accident triggers to the sequence of events and the outcomes of an accident. Particularly, a set of related keywords in the context of the narrative can represent how the accident proceeded. Previous studies on text analytics for structuring accident reports have been limited to extracting individual keywords without context. We proposed a context-based analysis using a Natural Language Processing (NLP) algorithm to remedy this shortcoming. This study aims to apply Word2Vec of the NLP algorithm to extract adjacent keywords, known as word embedding, conducted by the neural network algorithm based on supervised learning. During processing, Word2Vec is conducted by adjacent keywords in narrative texts as inputs to achieve its supervised learning; keyword weights emerge as the vectors representing the degree of neighboring among keywords. Similar keyword weights mean that the keywords are closely arranged within sentences in the narrative text. Consequently, a set of keywords that have similar weights presents similar accidents. We extracted ten accident processes containing related keywords and used them to understand the risk factors determining how an accident proceeds. This information helps identify how a checklist for an accident report should be structured.

Experimental and analytical investigations are performed to explore the explosion characteristics of a hydrogen-air mixture in open atmosphere. A hydrogen-air mixture tent of total volume of 27 m³, with 40% hydrogen volume, is used to observe overpressure at a distance from the ignition source. Vapor cloud explosion analyses are performed using the TNO multi-energy model and Baker-Strehlow-Tang model. The results of these analyses are compared with experiment done from this study and references. The experimental results with and without obstacles indicate that the overpressure values measured at a distance of 4.5-21.5 m from the ignition source are about 9.4-3.6 kPa and 6.5-2 kPa, respectively. This implies that the overpressure with obstacles is approximately 1.7 times greater than that without obstacles. Analytical observation indicates that the results obtained with the Baker-Strehlow-Tang model with M_f = 0.2-0.35 are in good agreement with those of most of the previous studies, including that obtained from this study. Moreover, the TNO multi-energy model with a volume of 27 m³ well predicts the overpressure obtained from this study. Further studies should considered explosions in semi-confined spaces, which is more suitable for hydrogen refueling stations.

This basic study introduces the concept of S-BRT and develops dwell time estimation models that consider road geometry and S-BRT characteristics for a signal operation strategy to meet the S-BRT's operational goal of high speed and punctuality. Field surveys of low-floor buses similar in shape to S-BRTs and data collection of passengers, station elements, vehicle elements, and other factors that can affect stop times were used in a regression analysis to establish statistically significant dwell time estimation models. These dwell time estimation models are developed by categorizing according to the locations of the signal or sidewalk that have the most impact on the dwell time. In this way, the number of people boarding and alighting the bus at the crowded door and the number of people boarding and alighting the bus at the front door considering the internal congestion was analyzed to affect the dwell time. The estimation dwell time models in this study can be used in the establishment of strategies that provide priority signals to S-BRTs.

Radionuclides, particularly radioactive cesium (Cs), are a concern of human health in some nuclear power accidents. It could lead to a high level of intracellular accumulation due to its high radioactivity and long half-life. Therefore, it is imperative to develop a method to remove Cs from wastewater. Herein, we synthesized activated carbon fibers (ACFs) doped with Prussian blue (PB) via in situ methods. We classified samples by their preparation method as either physical (PB-ACF-A) or physicochemical (PB-ACF-B) syntheses for comparison. The PB-ACF-B sample showed a significant surface loss compared to PB-ACF-A but a better ¹³³Cs adsorption capacity. All samples fit well to Langmuir isotherms and the values of qmax were directly correlated to the amount of PB on the surface of the ACFs. Adsorption characteristics were further confirmed by the calculated free energy, enthalpy, and entropy.

Lecture rooms are crowded with many attendees. Moreover, they rely significantly on the natural ventilation through windows for removing and controlling indoor contaminants such as CO₂. With the aim of broadening the understanding of the characteristics of natural ventilation phenomena in lecture rooms, the average individual CO₂ release rates of attendees were measured during the course of a lecture and compared with previously reported CO₂ release rates. In addition, the effects of natural ventilation through windows on the time-variant CO₂ concentrations in the center of the lecture room were measured and analyzed. Moreover, details about the overall and regional CO₂ concentrations, as well as the air flows in the lecture room, were simulated and analyzed with computational fluid dynamics software, Fluent 2020 R2. It was found that the average individual CO₂ release rates were slightly slower than previously reported rates. The local CO₂ concentrations in the lecture room for regions with a high density of attendees increased over a short period of time, although the natural ventilation was already started by opening the windows. The overall CO₂ concentration in the lecture room rapidly decreased in the early stage of ventilation, but declined very slowly after a longer period of ventilation time. Therefore, in order to enhance the efficiency of a lecture room's natural ventilation, it is recommended to homogeneously distribute the attendees in the lecture room, and to frequently open the windows for short periods of time.