Gravel is used in railway infrastructure to reduce environmental impacts and noise, but gravel on tracks must be replaced continuously because it deforms due to wear and weathering. It is therefore necessary to review the entire railroad life cycle. In this study, an unmanned aerial vehicle (UAV) was used to measure resuspended dust over a wide area. The dust was generated from transport movements in relation to the operation of a quarry, which represents the first stage of the railway life cycle. The dust was measured at Gangwon-do quarry using a Sniffer4D module, which can provide measurements at 1 s intervals through a light scattering method and has high reliability (R2 = 0.95 for PM2.5, R2 = 0.88 for PM10). The hourly generation of fugitive dust was calculated as 2937.5 g/h for PM2.5 and 4293.2 g/h for PM10. The social cost of dust generation was calculated as KRW 36.59 billion. The amount of dust generated per hour at the quarry was ~12 times greater than that generated by the operation of a regulator as a maintenance vehicle, with the largest amount of fugitive dust generated by the washing-type vehicle. This is the first study to measure the amount of fugitive dust generated in real time at 1 s intervals by monitoring the first stage of the railroad life cycle over a wide area using a Sniffer4D module attached to a UAV. This method can be replicated for use in various studies.

In Korea, concentrations of particulate matter (PM10) are significantly higher in urban railway tunnels (178.1 μg/m3) than in metropolitan areas (49 μg/m3). In railway tunnels in Korea, it was maintained at 3–4 times higher concentration than general atmosphere and platform. Dust generated by trains is scattered at high speed in these tunnels, making filtration difficult; therefore, the development of filters that can be maintained in tunnels is required. In the present study, we examined PM adsorption in the laboratory scale using activated carbon fiber (ACF), which has high adsorption and capacity. The ACF depth, velocity of flow, and fine PM concentration in the tunnel were the experimental variables. We compared PM concentrations before and after the filter experiments, and calculated removal efficiency to determine the optimal conditions. Comprehensive examination of the experimental variables and differential pressure showed that the optimal conditions for an ACF specimen were a wind speed of 3.0 m/s and the ACF depth of 400 mm. The average removal efficiency of PM10 was 55.5%, and that of PM2.5 was 36.6%. The reproducibility tests showed that the ACF filter could be washed and reused and is suitable for various places because it is easily maintained.

Human activities, including walking, generate an airflow, commonly known as the slipstream, which can disperse contaminants indoors and transmit infection to other individuals. It is important to understand the characteristics of airflow to prevent the dissemination of contaminants such as viruses. A cylinder of diameter 500 mm, which is the average shoulder width of an adult male, was installed in a motorcar and moved at a velocity of 1.2 m/s, which is the walking speed of an adult male. The velocity profile of the slipstream generated during this movement was measured by locating the sensor support at 0.15–2.0 m behind the cylinder. The wind velocity was set to 1.2 m/s to conduct the numerical analysis. The measurement data revealed the velocity profile of the space behind the cylinder, and a comparison of the numerical analysis and the measurement results indicate very similar u (measured velocity) / U (moving velocity) results, with a maximum difference of 0.066, confirming that the measured values were correctly estimated from the results of the numerical analysis.

Digital transformation projects have been undertaken in the land transportation and railway industries, including the introduction of various smart construction technologies. With the expansion of policies to increase the share of railway transportation as an environmentally sustainable means of transportation that meets the needs of the carbon-neutral era, 3D digital information is required throughout the entire chain of railway construction, route selection, status analysis, design, construction, and maintenance. The need for scientific and rational decision making is increasing. In this study, based on point cloud data acquired by an unmanned aerial vehicle (UAV) and a handheld mobile device, the landscape infrastructure around a railway was digitally converted, and a railway Landscape Information Model (LIM) process that modeled various types of landscape information was derived. Additionally, through the voxelization of 3D data, information regarding a railway’s surrounding environment, analyzed as a 3D volume concept and a convergence plan with deep-learning-based artificial intelligence (AI) technology, was presented through object recognition using a clustering algorithm. A railway LIM dataset could be created from a total of seven major categories, and massive data processing through AI convergence will be a future possibility through optimization of the point cloud data clustering algorithm. The future of the railway industry requires the establishment of a railway LIM for the integrated management of a railway’s surrounding environment and building information modeling (BIM) of structures such as tunnels. The railway LIM process has potential for use in various fields, such as environmental management and safety improvement for disaster prevention.

In accordance with the paradigm of the 4th and Industrial Revolution, the introduction of building information modeling is expected in all areas related to railroad construction, operation and management, along with the establishment of a metaverse platform that combines big data, the Internet of Things, and artificial intelligence. The performance of tasks related to the safety and maintenance of railway facilities is aided by the use of digital systems free from physical and temporal constraints. Three-dimensional (3D) modeling and other 4th industrial technologies, such as unmanned aerial vehicles (UAVs) and light detection and ranging (LiDAR), are increasingly implemented in many types of infrastructure. With respect to railroads, the use of these methods to monitor tunnel spaces has been hindered by the limitations of modeling with UAV and inadequate Global Positioning System reception. Here, we conducted the domestic application of 4th industrial technologies to a railway tunnel; we addressed these problems using a BLK360, a fixed LiDAR device that combines two-dimensional panoramic images and a 3D point cloud method. The outcomes of this research will benefit railway operation managers by providing a platform combining a two-dimensional panoramic virtual reality (VR) image and a 3D model developed from a 3D scan framework for the maintenance of existing railway facilities (tunnels). Our approach was optimized for the maintenance and operational management of railroad facilities, as demonstrated for tunnels, because it continuously acquires time-series data that is appropriate for the maintenance of the corresponding space. In the future, this approach can be used for test tracks and operational lines.