https://doi.org/10.12652/Ksce.2025.45.3.0287

이기상(Lee, Ki-sang);곽대열(Kwak, Dae-yeol);정재훈(Jeong, Jae-hun);박기정(Park, Ki-jung);손창호(Sohn, Chang-ho)

This study aims to analyze the relationship between temperature and structural displacement using long-term monitoring data from the Cheongpung Bridge, a hybrid cable-stayed bridge located in Jecheon, South Korea. Displacement data were collected from GNSS, laser displacement sensors, and expansion joint sensors, while temperature data were obtained from thermometers installed at both the tower and girder sections. Correlation analysis revealed that the coefficients were consistent across different thermometer types, indicating minimal influence from installation conditions. Notably, both the transverse displacements of the bridge and the vertical displacements of the girder exhibited strong correlations with temperature. To validate these findings, a finite element analysis model was constructed using Midas software, confirming that the bridge components responded differently to thermal variations: the main towers and expansion joints moved in opposite transverse directions, and the center span showed downward deflection. Linear regression models were subsequently developed for each structural component based on girder temperature, which showed the highest correlation. The training period was varied in one-year increments, and despite differences in data completeness, most models achieved determination coefficients (R²) above 0.6. These results demonstrate the feasibility of temperature-based prediction of long-term displacement behavior in cable-stayed bridges.

https://doi.org/10.12652/Ksce.2025.45.3.0297

신서연(Shin, Seoyeon);윤누리(Yun, Nu-ri);박건(Park, Gun);홍기남(Hong, Kinam)

Box-type structures have been widely employed in civil engineering from ancient times to the present, serving essential roles in the construction of various infrastructure systems such as water supply and drainage, urban gas lines, electricity and telecommunications, highways, and railways. Their usage is expected to increase further in both frequency and scale. Currently, the FEM is the predominant analytical technique used to evaluate the safety of these structures. While FEM offers precise and reliable results, it often demands considerable time and effort due to the complexity of modeling and simulation, particularly when applied to large-scale structures. This can lead to inefficiencies in the design and review process. To address these limitations, this study performed both static and seismic analyses of box structures using FEM, and subsequently utilized the resulting data to train a deep learning model aimed at predicting structural member forces. A total of 600 numerical models were developed using MIDAS software. The predictive performance of the deep learning model was assessed using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). The results indicated that the model's performance under static loading conditions was superior to that under seismic conditions, which may be attributed to the complexity introduced by dynamic responses. This study highlights the potential of deep learning as a complementary approach to traditional FEM-based structural analysis. The proposed methodology offers a promising avenue for enhancing the efficiency of structural safety assessments and design processes in future engineering applications

https://doi.org/10.12652/Ksce.2025.45.3.0305

이상엽(Lee, Sang Yeop);아시프 라비아(Asif, Rabea);강민혁(Kang, Min Hyeok);심원섭(Shim, Won Sup);허종완(Hu, Jong Wan)

This study conducted an optimization design using finite element analysis to assess the structural stability of modular pipe racks in unconventional oil plants, which have recently garnered attention, and to improve their fatigue life. The subject of the study is the modular pipe rack of a 300BPD oil sands plant built in Yeoncheon, with the target site selected as Alberta, Canada, a major oil sands deposit region prone to frequent seismic activity. Dynamic analysis of earthquake loads, considering design standards (ASCE, NBC) and local environmental conditions, revealed a potential for block shear failure at the gusset plate. Consequently, an optimization design was performed to alleviate stress concentration and improve fatigue life by modifying the shape of the gusset plate, improving the joint with the web, and reinforcing the structure. Fatigue analysis under earthquake loads showed that two optimized models had fatigue lives at the stress concentration and bolt joint areas at least 400 % longer than the original. This study distinguishes itself from previous research by applying dynamic analysis based on actual time-history data for the entire model, rather than being limited to joint areas. The results of this study are expected to serve as dynamic analysis guidelines and optimization design criteria for ensuring the structural stability and economic viability of modular pipe racks in the future.

https://doi.org/10.12652/Ksce.2025.45.3.0313

김민성(Kim, Min Seong);김홍주(Kim, Hongju);강준원(Kang, Jun Won)

This study presents a full-waveform inversion (FWI) framework that utilizes flexural wave measurements to reconstruct the elastic modulus and soil stiffness profiles of prestressed lifeline structures embedded in the ground. The forward problem of a flexural wave propagation in the prestressed beam-soil system is formulated as an initial-boundary value problem, incorporating the Euler-Bernoulli beam equation with a prestressing term and the Winkler foundation model. The dynamic flexural response of a beam supported by an elastic foundation is numerically computed using the Galerkin finite element method with C1 shape functions and the Newmark-β time integration scheme. The inverse problem for reconstructing the material properties of the prestressed beam-soil system is formulated as a partial differential equation-constrained optimization problem, where the objective is to minimize the difference between measured and computed deflections to determine the optimal values of the beam’s Young’s modulus and soil stiffness. The optimization process employs a Lagrangian functional, integrating the objective functional and constraints, and derives the state, adjoint, and control problems from the first-order optimality conditions of the Lagrangian. These problems are iteratively solved in a reduced space of the control variables to reconstruct the Young’s modulus and the soil stiffness. Numerical examples are presented for two cases: (1) reconstruction of the beam’s Young’s modulus when the soil stiffness is known, and (2) reconstruction of the soil stiffness when the beam’s Young’s modulus is known. The effect of regularization during the inversion on the reconstruction accuracy is presented, and the results show that, in all cases, the reconstruction error remains below 0.06 % of the initial values, confirming the accurate recovery of the material profiles. The methodology presented in this study can be applied to the condition assessment of various prestressed lifeline structures, such as water pipelines.

https://doi.org/10.12652/Ksce.2025.45.3.0325

한형석(Han, Hyoungseok);정인우(Cheong, In Woo)

The development of high-performance latex-modified concrete (LMC) for bridge deck maintenance requires a careful balance between rapid strength gain, long-term durability, and environmental resilience. This study proposes an optimized eco-friendly styrene-butadiene (SB) latex system tailored for ultra-rapid hardening concrete (URHC) applications. The concrete formulation incorporates elevated levels of calcium (Ca2?) and aluminum (Al3?) ions to accelerate cement hydration, achieving an early compressive strength of 21 MPa within 4 hours?suitable for time-sensitive bridge deck repairs. To enhance colloidal stability and dispersion behavior of the latex in alkaline environments, 2 wt.% N-methyl acrylamide and 1 wt.% acrylic acid were introduced through seeded emulsion polymerization. These functional monomers improve hydrogen bonding and steric hindrance, contributing to uniform film formation and latex compatibility in cementitious matrices. The butadiene/styrene ratio was adjusted to 40/60, resulting in a glass transition temperature (Tg) of approximately 0°C. This adjustment enhances flexibility, impact resistance, and crack bridging ability. Furthermore, the average particle size of the SB latex was reduced from 190 nm to 160 nm, significantly improving mechanical interlocking, flexural strength, and substrate adhesion. The reduction in particle size also contributed to increased resistance against chloride ion penetration, a key factor in protecting reinforcing steel. A high gel content exceeding 85 % was achieved, which effectively improved freeze-thaw durability and minimized phase separation under cyclic environmental stresses. The synergistic effects of chemical structure, particle engineering, and hydration control demonstrate that the proposed SB latex-enhanced URHC is a robust and sustainable solution for rapid bridge deck rehabilitation, offering both immediate mechanical performance and extended service life under aggressive exposure conditions. These results highlight the potential of the proposed latex system to strengthen infrastructure resilience and extend service life, while contributing to the sustainability of bridge rehabilitation by enabling faster construction, reducing material waste, and improving long-term durability under aggressive exposure conditions.

https://doi.org/10.12652/Ksce.2025.45.3.0339

서호철(Seo, Hocheol);김희철(Kim, Heechul);최수연(Choi, Suyeon);김연주(Kim, Yeonjoo)

The frequency and intensity of extreme weather events are increasing due to climate change, and precipitation patterns are becoming more irregular. These changes have led to a rise in flood damage in urban areas, highlighting the growing importance of short-term rainfall forecasting technologies that can accurately predict rainfall intensity and location within a limited time frame. This study evaluates the rainfall prediction performance of ConvLSTM and pySTEPS models using radar-based rainfall data in the Andong Dam watershed (128 km×128 km) in South Korea. This study specifically analyses how different input domain sizes (128 km×128 km, 256 km×256 km, and 384 km×384 km) affect the performance of precipitation prediction over the same spatial domain, the Andong Dam basin. The results show that the prediction performance of both models improved as the input domain size increased. In particular, the pySTEPS model using a 384 km×384 km input domain exhibited relatively superior rainfall prediction performance for lead times exceeding 80 minutes. Therefore, this study suggests that the selection of an appropriate input domain for radar-based rainfall prediction models significantly impacts prediction performance, providing important guidelines for improving forecasting accuracy in future applications.

https://doi.org/10.12652/Ksce.2025.45.3.0347

윤성심(Yoon, Seong-sim);최지안(Choi, Gian)

One of the primary objectives of predicting river water levels is to establish criteria for issuing flood warnings and alerts. This study aims to optimize the training data for a deep learning-based river water level prediction model and enhance its accuracy by utilizing AutoKeras, which supports automatic design and optimization of deep learning models, to develop models that minimize artificial influences. The upper basin of the Hantan River was selected as the study area, and datasets were constructed using water level data from three observation stations and mean areal rainfall data. Based on these datasets. Based on these datasets, Two models were developed: Model 1 was trained on datasets that included all recorded rainfall events, while Model 2 was trained on datasets capturing significant water level increases. Predictions for Hantan Bridge indicated that Model 1 achieved higher accuracy in time-series water level estimation, as evidenced by a higher correlation and lower RMSE. In contrast, Model 2 exhibited superior flood detection capability, showing higher recall, F1-score, and CSI. These results highlight the importance of selecting appropriate training data when developing deep learning models, particularly for flood prediction. Emphasizing critical factors such as water level rises can enhance model performance, enabling more effective early warning systems and improving disaster preparedness.

https://doi.org/10.12652/Ksce.2025.45.3.0359

정일문(Chung, Il-Moon);이정우(Lee, Jeongwoo)

This study proposes institutional and technical improvements for integrated water resources management with considering the impact of grounwater use in river-adjacent areas on streamflow. Hydrological connectivity between groundwater and surface water can significantly affect river ecosystems and downstream stakeholders, particularly during the dry season. However, current management framework in South Korea treats groundwater and surface water as separate systems, limiting its ability to evaluate and respond to impacts, such as massive abstractions. This study presents pilot applications of the SWAT-MODFLOW integrated model and impact-zone mapping techniques. It also emphasizes the need for institutional reform, including harmonization of the Groundwater and River Acts, establishment of a joint permitting system, and introduction of a total quantity control scheme. Drawing on international case studies, this study reinterprets the key elements of integrated management in the Korean context and outlines a transition pathway toward integrated water governance based on scientific modeling, legal reform, and stakeholder engagement.

https://doi.org/10.12652/Ksce.2025.45.3.0365

김우영(Kim, Woo Young);안제영(An, Je Young);이원효(Lee, Won Hyo);경갑수(Kyung, Kab Soo);김태형(Kim, Tae Hyung)

The pullout capacity of the suction anchor of a floating offshore wind turbine is crucial for the maintenance and operation of the structure. Therefore, various factors are considered when designing and installing the anchor. Among these factors, this study investigated the effect of seabed inclination on the pullout capacity of the suction anchor was investigated using both geometric methods and finite element analysis. According to geometric analysis, unlike flat ground, shear stress (τ) occurs on inclined seabed, and the seabed inclination angle (α) directly affects the horizontal and vertical components of the load, altering the horizontal force (H) and vertical force (V), which leads to a reduction in the pullout capacity of the anchor. The results of numerical analysis also showed, similar to the geometric analysis, that the pullout capacity of the anchor decreases as the seabed inclination increases. However, unlike the geometric analysis, the numerical analysis reflects characteristics such as the cohesion of the ground and the interface elements between the anchor and the ground, resulting in different outcomes from the geometric analysis. Based on the results of both methods, the allowable seabed inclination was determined to be 5°, considering the reduction in pullout capacity. For steeper inclinations, further geometric or numerical analysis is required. As a result, since seabed inclination is significantly related to the pullout capacity of a suction anchor for a floating offshore wind turbine, it is necessary to consider the change in pullout capacity based on the inclination angle during installation.

https://doi.org/10.12652/Ksce.2025.45.3.0377

이주호(Lee, Juho)

With the rapid advancement of cutting-edge technologies such as artificial intelligence (AI) and robotics, the domestic logistics environment is undergoing significant transformation. However, in business-to-business (B2B) freight transportation, paper-based transactions still dominate, posing challenges for IT integration. On the other hand, the increasing use of mobile devices has expanded the necessity for adopting electronic documents, leading to active participation from national R&D projects and IT enterprises. Currently, the Ministry of Land, Infrastructure, and Transport (MOLIT) is promoting the development of digital information standard-based transaction, sharing, and management technologies for land freight transportation through its High-Value-Added Convergence Logistics Delivery and Infrastructure Innovation Technology Project. As part of this initiative, the development of an electronic document system for corporate logistics and freight transportation is being pursued. This study aims to analyze strategies for applying electronic documents in corporate logistics and freight transportation, deriving implications for technology dissemination and policy recommendations. In particular, it examines the processes required for electronic document adoption by key stakeholders in transportation transactions, including shippers, freight forwarders, carriers, and drivers. Additionally, it reviews the limitations of traditional paper-based workflows and presents key challenges for the widespread adoption of electronic documents. The findings of this study are expected to accelerate the digital transformation of the domestic logistics industry and establish a foundation for integration with autonomous driving and smart logistics technologies.

https://doi.org/10.12652/Ksce.2025.45.3.0385

안홍기(An, Hong Ki);김동선(Kim, Dong Sun)

Congestion or delays occurring at intersections can significantly impact adjacent roadways, potentially leading to widespread urban traffic congestion. Accordingly, intersections should not be viewed merely as points of connection between roads, but rather as critical control nodes within the broader transportation system. Minimizing congestion at intersections is thus a major challenge for transportation engineers. In recent years, growing interest in artificial intelligence(AI) algorithms has led to increased research on intelligent traffic signal control systems aimed at enhancing intersection performance. Many studies have focused on multi-objective optimization models employing AI-based algorithms such as Genetic Algorithms(GA) and Particle Swarm Optimization(PSO), targeting reductions in travel time, delays, and the number of stops, or improvements in intersection capacity. This study proposes a hybrid Tabu Search-Ant Colony Optimization algorithm that incorporates CO2 emissions as a key objective within a multi-objective framework. The proposed algorithm was applied to a signalized intersection in Alor Setar, Malaysia, and its performance was evaluated using SIDRA analysis. The optimized signal cycle length of 116 seconds resulted in a 20 % reduction in queue length, an 18 % reduction in CO2 emissions, a 25 % decrease in the number of stops, and a 15 % improvement in intersection capacity. These findings provide empirical evidence that the proposed algorithm is effective in achieving sustainable and efficient traffic signal control.

https://doi.org/10.12652/Ksce.2025.45.3.0395

김문정(Kim, Moon Jeong);김시곤(Kim, Si Gon)

Since 2013, Seoul has operated a late-night bus service, known as the “Owl Bus”, to improve nighttime mobility. The service was designed based on demand forecasting using mobile communication-based floating population data and has demonstrated high usage levels since its introduction. However, the route design, which primarily focuses on commercial areas, raises concerns about spatial accessibility and equity for residential neighborhoods. This study empirically examines spatial disparities in late-night bus accessibility across different residential types in Seoul. Residential areas were categorized into apartment-type, non-apartment-type, and vulnerable housing using building registry data, and late-night bus stop locations were mapped. A 500-meter walkable buffer was established around each stop to determine the number of residential buildings by type within the accessible area. Accessibility ratios were then calculated for each district, and inter-type differences were quantified using a “Coverage Rate Gap” metric. The results show that the average accessibility rate in Seoul is 66.5 %, with district-level values ranging from 34.6 % (Nowon-gu) to 98.1 % (Jung-gu). Notably, Seongdong-gu exhibited an accessibility gap of up to 30 percentage points between housing types, suggesting that disparities are influenced not only by residential form but also by geographic location. These findings underscore the importance of incorporating spatial equity considerations into the design of nighttime transit systems.

https://doi.org/10.12652/Ksce.2025.45.3.0403

원태연(Won, Taeyeon);조수민(Jo, Su Min);정지헌(Jung, Ji Heon);장명도(Jang, Meongdo);김용민(Kim, Yongmin)

Nightyime image serves as critical visual information in a wide range of applications, including disaster response and surveillance operations. To improve the clarity and visibility of such imagery, extensive research has been conducted on imaging devices, sensor technologies, and image processing techniques. This study proposes a method to simulate daytime images from nighttime image acquired by various sensors by learning object-specific feature regions within the image. Considering that temperature differences between daytime and nighttime images vary depending on the object, a CNN-based unsupervised segmentation technique, and the UNSB (Unpaired Neural Schrodinger Bridge) model were applied. The experimental setting for the unsupervised segmentation technique was designed to satisfy three constraints: feature similarity, spatial continuity, and a limited number of clusters. Training data were composed by compositing thermal infrared images, LiDAR intensity images, LiDAR range images, and segmentation images, which were then learned in conjunction with corresponding optical images using the UNSB model to simulate daytime optical image. The experimental results showed SSIM 0.593, PSNR 14.34, and R² 0.218 based on 120 epochs of training. For each object detail, the colors were similar to the original image, proving that the conversion result is excellent when the object features are clear.

https://doi.org/10.12652/Ksce.2025.45.3.0413

신한철(Shin, Han-chul);김시곤(Kim, Si-gon)

This study systematically analyzed the correlation between the types of railway accidents and their underlying causes in order to enhance the overall efficiency of railway safety management. Furthermore, after examining this correlation, the study conducted an in-depth analysis of how accident causes affect the scale of economic damage. By including a wide range of railway systems?such as high-speed rail, conventional rail, and urban rail transit?as the subjects of analysis, the research sought to identify, from multiple perspectives, whether there are statistically significant relationships between accident types and key contributing factors, including human error, technical failure, and external influences. To this end, data preprocessing and coding were carried out by defining the type of railway vehicle, the cause of the accident, and the amount of economic damage as nominal variables. A thorough homogeneity of variance test was then performed to ensure the reliability and validity of the statistical analysis. In addition, a variety of multivariate statistical techniques were applied to analyze the interaction effects between railway type and accident causes, as well as the resulting differences in economic losses. The results of this study are expected to serve as a foundational resource for establishing practical policies to strengthen railway safety management and for developing more precise accident prevention strategies. Furthermore, they offer meaningful implications for guiding future research in related fields and are anticipated to make a substantial contribution to the development of customized safety management systems by railway operating agencies.

https://doi.org/10.12652/Ksce.2025.45.3.0421

노희구(Noh, Hee Koo);김시곤(Kim, Si Gon)

This study presents the necessity and construction plan of an integrated simulation training system for railroad workers. For safe and efficient rail operation, it is important to develop the ability of railroad workers to respond quickly and effectively in various situations. Existing education and training systems are based on theory, so they lack practical experience, and as a result, workers often lack the practical skills and capabilities necessary to perform their duties. Therefore, this study aims to provide railway workers with learning opportunities in an environment similar to the real world through an integrated simulation training system using the latest technology. This system will consist of an e-learning platform, VR (virtual reality) and AR (augmented reality) technologies, and various simulation modules to enable comprehensive education. In the study, first, the problems of the existing education and training related to railroad work are analyzed, and the necessity of an integrated simulation training system is specifically revealed. Next, the system components and functions, the education curriculum integration plan, and the step-by-step implementation strategy are presented in detail. Finally, this study concludes by declaring the expected effect that the new education and training system will contribute to improving the job ability of workers and preventing safety accidents. The results of this study are expected to provide practical guidelines for effective education and training for railway workers in the future