Kwang-Kyun Lim

This study investigates the optimal service life of urban Electric Multiple Units (EMUs) by integrating two complementary evaluation methods: economic service life and maintenance limit life. Using a comprehensive dataset from Seoul Metro—including 498 trainsets and 3554 overhaul records—this research examines the relationship between long-term maintenance costs, depreciation, and residual values. The economic service life is derived by minimizing the average equivalent annual cost (AEC), while maintenance limit life is assessed based on government guidelines that define cost-inefficiency thresholds. The analysis finds that the average economic service life for EMUs on Lines 1–4 is approximately 39 years—substantially exceeding the traditional 25-year benchmark used in past replacement policies. Maintenance limit life, based on permissible cost ratio thresholds, extends up to 47 years in some cases. Sensitivity analysis indicates that maintenance cost variations exert a greater influence on optimal service life than discount rate assumptions, highlighting the importance of strategic maintenance management. The proposed dual-framework approach demonstrates the limitations of rigid, statutory-based replacement planning and supports a transition toward data-driven, line-specific decision-making. The findings provide actionable insights for transit authorities and policymakers seeking to improve capital investment efficiency and optimize lifecycle management of urban rail assets. Beyond economic efficiency, the study contributes to sustainability by supporting resource-efficient asset utilization, reducing premature disposal of serviceable rolling stock, and lowering lifecycle carbon emissions associated with manufacturing new vehicles. The proposed framework thus offers a practical basis for integrating economic and environmental considerations in sustainable urban rail asset management.

Railroads play a pivotal role in the Korean national economy, necessitating a thorough understanding of factors influencing accidents for effective mitigation strategies. Unlike prior research focused on accident frequency and severity, this study delves into the often-overlooked aspect of time delays resulting from railroad accidents. Analyzing 15 years of nationwide data (2008–2022), encompassing 3244 human-related and 3350 technical events, this research identifies key factors influencing delay likelihood and duration. Factors considered include event type, season, train type, location, operator size, person type involved, facility type, and causes. Despite an overall decrease in events, variable delay times highlight the need to comprehend specific contributing factors. To address excess zeros, the study employs a two-stage model and a zero-inflated negative binomial (ZINB) model, alongside artificial neural networks (ANNs) for non-linear pattern recognition. Human-related delays are influenced by event types, seasons, and passenger categories, exhibit nuanced impacts. Technical-related delays are influenced by incident types and facility involvement. Regarding model performance, the ANN models outperform regression-based models consistently in all cases. This study emphasizes the importance of considering both human and technical factors in predicting and understanding railroad accident delays, offering valuable insights for formulating strategies to mitigate service disruptions associated with these incidents.

The purpose of this research is to build a deep learning algorithm-based model that can use weather indicators to quantitatively predict financial losses associated with weather-related railroad accidents. Extreme weather events and weather disasters caused by global warming are happening with increasing frequency worldwide, leading to substantial economic losses. Railways, which represent one of the most important means of transportation, are also affected by such weather events. However, empirical and quantitative studies examining losses stemming from weather conditions for railways have to this point been scarce. Hence, the present study collected and analyzed weather-induced railway accident data and meteorological factors (wind, precipitation, rainfall, etc.) from 2001 to 2021 with the aim of predicting financial losses caused by weather events; the ultimate goal is to help inform long-term strategies for effective recovery from railway accidents. Objective and scientific analysis was conducted in the present study by using a deep learning algorithm. The outcomes and framework of this research will offer crucial guidelines for efficient and sustainable railway maintenance. These results will also serve as a crucial point of reference for loss quantification studies and other facility management studies.

So far, studies for predicting construction safety accidents have mostly been conducted by statistical analysis methods that assume linear models, such as regression and time series analysis. However, it is difficult for this statistical analysis method to reflect the nonlinear characteristics of construction safety accidents determined by complex influencing factors. In general, deep learning techniques are used to analyze the nonlinear characteristics of complex influencing factors. Therefore, the purpose of this study is to propose a framework for developing a deep learning model for predicting safety accidents for sustainable construction. For this study, 1766 cases of actual accidents were collected by the Korea Occupational Safety Authority (KOSHA) over the 10-year period from 2010 to 2019. Eight factors influencing accident prediction such as medical day, progress rate, and construction scale were selected. Subsequently, the predictive power between deep learning models and conventional multi-regression models was compared using actual accident data at construction sites. As a result, a deep neural network (DNN) improved predictive power by 9.3% in mean absolute error (MAE) and 10.6% in root mean square error (RMSE) compared to a conventional multi-regression model. The results of this study provide guidelines for the introduction of deep learning technology to construction safety management.