Gas leaks can cause disasters at process sites, including fires and explosions, and thus, effective gas-leak detection systems are required. This study investigated the limitations of conventional detectors and introduced an innovative ultrasonic sensor-based approach for continuous monitoring. A new configuration for a stationary remote ultrasonic gas-leak monitoring system is proposed. The selected material was 1-Butene. The detection probability was assessed through a simulation based on a gas-leak scenario, detailing the selection criteria for leak sites and simulation conditions. Computational fluid-dynamics (CFD) simulations were used to evaluate the detection capability of the existing system, whereas Monte Carlo simulations were used to compare it with the proposed ultrasonic system. The CFD simulation was performed by setting the lower detection limit of the concentration-measurement-type gas detector to 600 ppm, and the leak-detection time was approximately 8.895 s. A Monte Carlo simulation accounting for wind direction showed that the existing system’s average detection probability was 3.41%. By integrating the existing and new systems, the detection probability increased by 96.58% and 3.92% compared with that using only the concentration-measurement and ultrasonic measurement methods, respectively. These rates provide valuable insights for future applications in oil refining and petrochemical industries.

Road icing can cause large traffic accidents on highways because, unlike snowy roads, its location is difficult to identify and it can occur rapidly, even during rainy weather. In this study, the amount and location of road icing were modeled and simulated over time based on the system dynamics theory. The simulation is expressed on the geographic information system (GIS) and facilitates advance detection of the location and amount of road icing that occurs unexpectedly unlike previous studies. Modeling was designed to process spatial and meteorological data after combining them. The spatial data used for modeling were Hillshade, Water System, Bridge, and Road (Highway). Air temperature, cloudiness, vapor pressure, wind speed, and precipitation were used as meteorological data. The amount of road icing was estimated by scientifically designing the parameters related to its occurrence between spatial and meteorological data. Based on this, the amount of road icing by location was simulated per 1m² using the GIS. The simulation results showed that the amount of road icing that began to increase from AM 08:00 reached its peak (an average of 213.62 g/m²) at noon and then slowly decreased. Additionally, when simulated with GIS, the sum amount of road icing between AM 12:00 and PM 13:00 was a maximum of 1707.292 (g/14 h) and a minimum of 360.082 (g/14 h) for each location. Hypothesis testing was conducted on whether road icing significantly occurs at actual points vulnerable to traffic accidents. Based on the results, the average significance level was calculated to be less than 0.05. Therefore, the alternative hypothesis that the model can estimate road icing in vulnerable areas was adopted. The verified simulation can be useful data to government agencies (e.g., road traffic authority) in their programs to prevent traffic accidents caused by road icing.

Fog, freezing rain, and snow (melt) quickly condense on road surfaces, forming black ice that is difficult to identify and causes major accidents on highways. As a countermeasure to prevent icing car accidents, it is necessary to predict the amount and location of black ice. This study advanced previous models through machine learning and multi-sensor-verified results. Using spatial (hill shade, river system, bridge, and highway) and meteorological (air temperature, cloudiness, vapour pressure, wind speed, precipitation, snow cover, specific heat, latent heat, and solar radiation energy) data from the study area (Suncheon–Wanju Highway in Gurye-gun, Jeollanam-do, South Korea), the amount and location of black ice were modelled based on system dynamics to predict black ice and then simulated with a geographic information system in units of square metres. The intermediate factors calculated as input factors were road temperature and road moisture, modelled using a deep neural network (DNN) and numerical methods. Considering the results of the DNN, the root mean square error was improved by 148.6 % and reliability by 11.43 % compared to a previous study (linear regression). Based on the model results, multiple sensors were buried at four selected points in the study area. The model was compared with sensor data and verified with the upper-tailed test (with a significance level of 0.05) and fast Fourier transform (freezing does not occur when frequency = 0.00001 Hz). Results of the verified simulation can provide valuable data for government agencies like road traffic authorities to prevent traffic accidents caused by black ice.

In South Korea, where mountainous terrain accounts for a significant proportion of the country, tunnels are essential for reaching destinations via the shortest possible route. Tunnels are high-traffic areas with only two exits, making them vulnerable to large-scale disasters in the event of a fire. However, existing longitudinal-flow-type exhaust systems (jet fans) are insufficient for effective smoke exhaust during a tunnel fire. Herein, a new ventilation system is proposed that uses a device with nozzles for expelling high-speed winds to generate a more robust ventilation flow at a tunnel entrance. A computational fluid dynamics (CFD) analysis showed that a jet fan system had a wind speed of 1.52 m/s at the tunnel exit. This system removed a total of 37,053.9 kg of air in 600 s. By contrast, the proposed Bernoulli ventilation system had a wind speed of 1.85 m/s at the tunnel exit and removed 53,308.4 kg air in 600 s. The simulation results from a scaled-down model of the tunnel safety testing facility were compared with the CFD analysis results. The ratio of the wind speeds between the tunnel entrance and exit was 2.7:1, exhibiting a similar trend. In conclusion, this study experimentally verified a CFD analysis of a Bernoulli ventilation system and provided a direction for improving longitudinal-flow-type exhaust systems. The results can be a foundational resource for government agencies (such as the Road Traffic Authority) in improving current ventilation systems.

Black ice, a phenomenon that occurs abruptly owing to freezing rain, is difficult for drivers to identify because it mirrors the color of the road. Effectively managing the occurrence of unforeseen accidents caused by black ice requires predicting their probability using spatial, weather, and traffic factors and formulating appropriate countermeasures. Among these factors, weather and traffic exhibit the highest levels of uncertainty. To address these uncertainties, a study was conducted using a Monte Carlo simulation based on random values to predict the probability of black ice accidents at individual road points and analyze their trigger factors. We numerically modeled black ice accidents and visualized the simulation results in a geographical information system (GIS) by employing a sensitivity analysis, another feature of Monte Carlo simulations, to analyze the factors that trigger black ice accidents. The Monte Carlo simulation allowed us to map black ice accident occurrences at each road point on the GIS. The average black ice accident probability was found to be 0.0058, with a standard deviation of 0.001. Sensitivity analysis using Monte Carlo simulations identified wind speed, air temperature, and angle as significant triggers of black ice accidents, with sensitivities of 0.354, 0.270, and 0.203, respectively. We predicted the probability of black ice accidents per road section and analyzed the primary triggers of black ice accidents. The scientific contribution of this study lies in the development of a method beyond simple road temperature predictions for evaluating the risk of black ice occurrences and subsequent accidents. By employing Monte Carlo simulations, the probability of black ice accidents can be predicted more accurately through decoupling meteorological and traffic factors over time. The results can serve as a reference for government agencies, including road traffic authorities, to identify accident-prone spots and devise strategies focused on the primary triggers of black ice accidents.

With the escalation and complexity of disasters, casualties have increased in rural areas. This underscores the need to strengthen the capabilities of the Citizen Corps Active in Disaster (CAIND) as an effective disaster response mechanism in underdeveloped regions. This study quantitatively assessed vulnerabilities relative to CAIND’s capacity and evaluated the effectiveness of improvement measures. A Monte Carlo analysis using system dynamics was conducted under four scenarios: 100% (baseline), 120%, 140%, and 160% intervention. Results were visualized using geographic information systems. In the baseline scenario, average vulnerability was 0.132, decreasing to 0.126, 0.120, and 0.117 in enhanced scenarios. These reductions correspond to improvements of 4.55%, 9.09%, and 11.36%. The findings suggest that enhancing CAIND’s capacity significantly improves disaster response in rural areas. They also highlight the importance of collaboration among policymakers, community leaders, and researchers in developing effective disaster management strategies for vulnerable regions.