https://doi.org/10.12652/Ksce.2025.45.2.0131

유용신(Yoo, Yong-Shin) ; 김우석(Kim, Woo-Seok)

Water supply pipelines are typically buried underground, but additional pipelines in the form of bridge-hanging pipes exist to accommodate construction feasibility and site conditions. Externally exposed bridges-attached pipes are subject to significant damage if they are compromised, so regular maintenance is necessary. But inspection of Pipelines attached to bridges is making it practically infeasible to conduct regular checks because of accessibility issues. This study analyzes the displacement and behavioral characteristics to bridges-attached pipe by utilizing drones and LiDAR technology, by minimizing the need for inspector access. When building 3D modeling using drones, It is difficult to obtain data when scanning objects due to environmental factors and the precision of the 3D modeling decreases. To compensate for this, a LiDAR device is added to the drone to improve data quality, which is then processed and analyzed to create a 3D digital visualization. The errors were verified through periodic surveys, and the displacement and behavior (contraction and expansion ) of bridges-attached pipes from the support frame were examined according to seasonal changes (summer to winter). Additionally, a root cause analysis was conducted for improper movements (detachment). Also, it analyzes the causes of improper movements (detachment) and proposes maintenance through repairs.

https://doi.org/10.12652/Ksce.2025.45.2.0139

김충길(Kim, Chunggil) ; 이유재(Lee, Yujae) ; 이재훈(Lee, Jaehoon) ; 방건혁(Bang, Geonhyeok) ; 허광희(Heo, Gwanghee)

In this study, a state-space equation based hybrid digital twin system was developed as a novel approach for bridge safety assessment. To overcome the data overload issue faced by conventional 3D model-based digital twins, an optimized model was designed using response data from specific target points. For this purpose, structural characteristics were analyzed based on a finite element (FE) model, and the Guyan Reduction method was applied to construct a state-space equation incorporating mass, stiffness, and damping coefficients. The state-space equation model was designed to quantitatively analyze the dynamic behavior of the structure, with initial parameters calibrated using experimental data. Furthermore, real-time data feedback was integrated to continuously reflect changes in the bridge’s condition, and a multi-objective optimization algorithm was employed to enhance the model’s reliability. During the optimization process, mass, stiffness, and damping coefficients were adjusted to minimize discrepancies with experimental data, thereby improving the digital twin model’s accuracy in predicting structural responses. To evaluate the effectiveness of the developed hybrid digital twin, experiments were conducted on a scaled bridge model. Various loading conditions were applied during the experiment, and the model’s predicted results were compared with actual measured displacement data. The results demonstrated a high correlation between the digital twin model and the experimental data, with an error rate of less than 5 %. The state-space equation-based hybrid digital twin system enhances sensitivity to bridge condition changes by reflecting real-time structural responses and enables more precise maintenance and safety diagnostics through periodic state updates.

https://doi.org/10.12652/Ksce.2025.45.2.0151

손호준(Son, Ho-Jun) ; 한정우(Han, Jeongwoo) ; 김태웅(Kim, Tae-Woong)

As the risk of drought occurrence increases due to climate change, various research works are underway around the world to analyze drought propagation from various perspectives, This study identified the source and sink regions of the spatial propagation of meteorological drought and evaluated the potential effects of the source regions. The results of the propagation potential (PP), which quantifies the extent to which droughts onset in a specific region propagate to other regions, indicated that the source regions of drought propagation are seasonally located in similar areas. The results of the potential influence of source region (PISR), which quantifies represents the extent to which a specific region is affected by drought propagation from the other source regions, indicated that 99.49 % of the source regions had a PISR value of 0.5 or more, while 23.68 % of the sink regions had a value of 0.5 or more. The source regions with high PISR values have high intensity of drought propagation from other regions within the study area. Therefore, the source regions were identified to significantly interact with other source regions, playing an intermediary role in the spatial propagation of drought in the Nakdong River Basin. In addition, considering spatial autocorrelation of seasonal propagation potential, the propagation potential of spring was highest compared to other seasons. This indicates that the frequency of drought event and spatial drought propagation are more frequent in spring.

https://doi.org/10.12652/Ksce.2025.45.2.0161

정윤환(Jeong, Yoon-Hwan) ; 황윤성(Hwang, Yun-Seong) ; 홍승호(Hong, Seung Ho)

This study first estimates the risk and resilience of a community considering socio-economic factors, and then uses this to provide a comprehensive disaster assessment tool called the Socio-Economic Flood Vulnerability Index(SEFVI). The index was developed so that it can be used in disaster prevention policies such as improving disaster prevention performance targets. The Socio-economic Hazard(SH) assessment used a multi-criteria approach that quantifies flood hazard by integrating 4 socio-economic indicators that are weighted according to the results of correlation analysis with amount of flood damage. Meanwhile, Socio-economic Resilience(SR) is defined as the recovery ability of a community after flooding, and is evaluated through infrastructure, social, and economic factors. The weighting coefficients for each indicator are obtained by integrating them through correlation analysis with the recovery cost compared to the flood damage cost for 8 socio-economic indicators. The Socio-Economic Flood Vulnerability Index divides socio-economic hazard into resilience, and if the hazard is high but the resilience is high, the flood vulnerability index is adjusted slightly lower considering the impact, and if the hazard is low but the resilience is low, the flood vulnerability index is adjusted slightly higher, enabling the development of a more reliable index. This study is expected to provide a new index(SEFVI) for evaluating flood impacts and to contribute to improving flood damage response strategies.

https://doi.org/10.12652/Ksce.2025.45.2.0173

최성욱(Choi, Seongwook) ; 김준성(Kim, Junsung) ; 강형식(Kang, Hyeongsik)

Artificial intelligence has been increasingly applied across various academic fields and can be effectively utilized to predict the amount of floating debris generated in watersheds. An artificial neural network model was developed in this study to predict the accumulation of floating debris in dam basin. The annual floating debris collection data was used to estimate the floating debris accumulation. Key influencing factors for floating debris generation were identified by analyzing the mechanisms of floating debris occurrence. The final input variables for the artificial neural network model were selected using Pearson correlation coefficient analysis and included annual average inflow, annual maximum flood discharge, and watershed area. The artificial neural network model was trained and validated using floating debris data and various hydrological data corresponding to the key influencing factors. Model performance was confirmed through correlation analysis, ensuring adequate training and validation. However, the prediction performance was significantly lower in certain dam watersheds, primarily due to the low reliability of data collected during periods of minimal debris accumulation. A comparative analysis with an existing linear regression model was also conducted, and methods to improve the prediction accuracy of floating debris accumulation were explored. This study demonstrates that the artificial neural network model can reliably predict floating debris accumulation given an extensive dataset on floating debris generation.

https://doi.org/10.12652/Ksce.2025.45.2.0181

왕사철(Wang, Sizhe) ; 김지영(Kim, Jiyoung) ; 변성호(Byun, Sung Ho) ; 김동균(Kim, Dongkyun) ; 김태웅(Kim, Tae-Woong)

The ongoing increase in the global population and rapid technological advancements have led to rising electricity demand, resulting in greater uncertainty in energy supply. Although hydroelectric power generation is an important component of renewable energy, it is greatly affected by changing precipitation and runoff patterns due to global warming. To investigate the impact of climate change on the future hydroelectric power generation (HPG) of Soyanggang Dam and Chungju Dam, we integrated climate projections from four global climate models (CanESM5, ACCESS-ESM1-5, INM-CM4-8, IPSL-CM6A) using Bayesian Model Averaging (BMA), and predicted future HPG using Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) models. Changes in HPG according to different scenarios and time periods were investigated based on the analysis of variance (ANOVA). We calculated the Standardized Precipitation Evapotranspiration Index (SPEI) for the future period, identified drought events using run theory, and estimated the amount of increase or decrease in HPG according to drought. Overall results show that changes in HPG are most significant during the flood(Jul-Sep) season in drought years. Specifically, in drought years, the HPG of Soyanggang Dam during the flood season decreased by 10.8 % under the SSP2-4.5 scenario, whereas under the SSP5-8.5 scenario, the HPG during the annual(Jan-Dec), drought(Jan-Jun, Oct-Dec), and flood(Jul-Sep) season decreased by 30 %, 33.3 %, and 23.6 %, respectively. For Chungju Dam, The HPG during flood season decreased by 42.5 % under the SSP2-4.5, while under the SSP5-8.5, the HPG during the annual and flood season decreased by 12.4 % and 63.8 %, respectively. This is because precipitation and runoff patterns change seasonally according to climate change scenarios, and multipurpose dam managers need to ensure the stability of hydroelectric power considering these pattern changes.

https://doi.org/10.12652/Ksce.2025.45.2.0193

채지현(Chae, Jihyun) ; 이상민(Lee, Sangmin) ; 임정은(Lim, Jungeun)

Achieving carbon neutrality to combat climate change is a critical challenge in modern environmental technology, emphasizing the urgent need for innovative approaches in carbon capture, utilization, and storage (CCUS) technologies. In this study, an electrochemical continuous mineral carbonation process utilizing seawater desalination brine was developed to evaluate CO2 removal performance and elucidate carbonation mechanisms. Analysis of cation enrichment in the cathodic side of the seawater electrolysis reactor under varying retention times revealed a maximum 3.9-fold increase in Ca2+ concentration, experimentally demonstrating the improvement of cation availability in the mineral carbonation process. Evaluation of CO2 removal performance based on catholyte inflow rates (8-100 mL/min) indicated a significant linear correlation between inflow rate and CO2 removal, expressed as “CO2 removal = 0.16×catholyte inflow + 22.96”(R2=0.975). Under optimal operating conditions (seawater inflow rate: 15 mL/min; catholyte inflow rate: 100 mL/min), the continuous mineral carbonation process achieved a CO2 removal efficiency of 86.1 % and a removal rate of 46.9 mmol/hr over 4000 seconds. Furthermore, mineral carbonation experiments using Mg-depleted catholyte supernatant confirmed that the precipitated carbonate was high-purity calcite (CaCO3), as verified by XRD and EDS analyses. The elemental composition of the product closely matched the theoretical composition of calcium carbonate. These results highlight the significant potential of seawater-based CO2 reduction and integrated carbonation processes, offering promising prospects for the practical application of CCUS technologies in achieving carbon neutrality.

https://doi.org/10.12652/Ksce.2025.45.2.0203

곽인호(Kwak, In Ho) ; 위대형(Wi, Dea Hyung) ; 권도은(Kwon, Do Eun) ; 이병현(Lee, Byeong Hyeon)

Hydrogen is widely recognized as a representative clean energy source that does not emit carbon dioxide when used as an energy source. Currently, the estimation of hydrogen's carbon emissions primarily focuses on the emissions generated during the production stage, depending on the production method, and carbon footprint analyses are conducted accordingly. However, in order to use hydrogen in practice, various infrastructures must be established to store, transport, and ultimately utilize the produced hydrogen. Carbon emissions also occur during the construction and operation of these infrastructures, and these emissions must be measured as well. Therefore, it is crucial to systematically assess the carbon emissions not only from hydrogen production but also from storage, transportation, and utilization throughout the entire life cycle. The LCA (Life Cycle Assessment) methodology can be applied to evaluate carbon emissions across all stages from hydrogen production to utilization, and to conduct such an assessment, establishing a reliable LCI (Life Cycle Inventory) database is essential. Nevertheless, the environmental impacts arising from the construction and operation of various infrastructure facilities required for hydrogen production and utilization have not been adequately considered. Thus, it is necessary to develop an appropriate LCA methodology that comprehensively includes all processes from production to utilization. Accordingly, this study aims to develop a methodology for deriving a database list necessary for constructing an LCI DB applicable to the life cycle assessment of hydrogen technologies and hydrogen cities in the transportation and infrastructure sector and to present it in detail.

https://doi.org/10.12652/Ksce.2025.45.2.0213

박준영(Park, Junyoung) ; 이상민(Lee, Sangmin) ; 진가현(Jin, Gahyeon)

As climate change intensifies, wastewater reuse technologies are emerging as crucial solutions for future water treatment challenges, particularly in addressing the urgent need for high-purity water in the semiconductor industry. This study investigated the application of multi-UV-based advanced oxidation processes (AOPs) that incorporate persulfate (PS) and titanium dioxide (TiO2) for treating effluent from wastewater treatment plants to meet semiconductor industry water quality standards. The treatment characteristics and efficiency of UV/PS and UV/TiO2/PS processes were systematically evaluated in lab-scale batch reactors, focusing on key operational parameters including UV light intensity, PS dosage, and TiO2 concentration. In the UV/PS process, optimal conditions of 26 W UV output and 3.7 mM (= 1 g/L) PS dosage achieved a maximum total organic carbon (TOC) removal efficiency of 89.08 % with a second-order rate constant of 1.98×10-2 L/mg·min. However, when UV treatment was applied independently, the TOC levels in treated water showed unstable patterns with an increasing trend. To address this limitation, the UV/TiO2/PS process was employed at 39 W UV output with 3.7 mM PS and 2 g/L TiO2, which resulted in an enhanced TOC removal efficiency of 92.58 % and a second-order rate constant of 2.18×10-2 L/mg·min. Although the overall removal efficiency was only marginally improved by the addition of TiO2, its inclusion significantly enhanced the stability of the AOPs process, ensuring consistent performance during long-term operation. These findings provide valuable insights for the development of industrial water reuse technologies by applying AOPs to effluent from wastewater treatment plants.

https://doi.org/10.12652/Ksce.2025.45.2.0225

이원효(Lee, Won Hyo) ; 정나영(Jung, Na Young) ; 안제영(An, Je Young) ; 김대환(Kim, Dae Hwan) ; 김태형(Kim, Tae Hyung)

In this study, geometric calculation and numerical analysis were performed to determine the effect of misorientation on the pullout capacity of a suction anchor of floating offshore wind turbine. In geometric calculation method, the effect of the misorientation (β) of the suction anchor on the pullout capacity was found to be small, with a decrease of less than 3 % in the pullout capacity when the misorientation was within 20°, and up to 30 % for a misorientation (β) of 90°. The calculation of the pullout capacity through numerical analysis was compared and reviewed based on the plastic state of the ground. As a result, the displacement 0.1D (D: anchor diameter) criterion, where general shear failure occurs around the suction anchor, was evaluated to be more suitable for determining the pullout capacity of the anchor than the 15 % shear strain of the ground. In addition, the change in pullout capacity due to the misorientation of the suction anchor using the geometric method was very similar to the numerical analysis results. Therefore, in the case of silty sand soil condition applied in this study, even if numerical analysis is not performed on the occurrence of misorientation during actual anchoring, the reduction of the pullout capacity of the suction anchor can be easily derived using geometric method.

https://doi.org/10.12652/Ksce.2025.45.2.0237

박태광(Park, Tae-Kwang) ; 박주영(Park, Joo-Young) ; 강승찬(Kang, Seung-Chan) ; 김태형(Kim, Tae-Hyung)

Recently, maintenance costs are increasing in Busan New Port due to residual settlement exceeding the allowable settlement. As of 2022, most of the residual settlement in New Port through leveling exceeded the allowable settlement. In the case of container pier 5, a settlement of 47.5 cm occurred, which greatly exceeded the allowable settlement of 10 cm. The residual settlement problem is closely related to the functional deterioration due to the deformation in the cross-section of the horizontal drainage layer. In this study, thus, the deformation of the horizontal drainage layer due to ground improvement was examined using existing measured data and confirmed by measuring surface settlement, pore water pressure, groundwater levels, and multi-layer settlement in field test. As a result of analyzing the measurement data for each section of the Busan New Port, it was found that the tendency for settlement and heaving was significant in the early stage of embankment fill (height of 0 to 4.0 m). Field test measurements showed that if embankment management is properly performed (a one-time fill of 0.5 m, rate of 3.3 cm/day), the deformation of the horizontal drainage layer can be sufficiently controlled when the separation distance between water collection wells is 60 m, ensuring no issues with its function. In other words, there was no rapid settlement or heaving of the horizontal drainage layer due to the fill, and the cross-section of the horizontal drainage layer remained intact. The measurement results confirmed that proper initial embankment management prevents cross-sectional deformation that would impair the horizontal drainage layer.

https://doi.org/10.12652/Ksce.2025.45.2.0251

박진국(Park, Jin Gook) ; 김성일(Kim, Sung Il)

Drawing lessons from international complex terrorist incidents targeting multi-use facilities, such as the 9/11 attacks, this study highlights that highly interconnected modern societies are particularly vulnerable to complex terrorism escalating into broader societal disasters if initial responses fail to integrate societal disaster vulnerabilities comprehensively. South Korea, where counter-terrorism systems have historically focused independently on protecting hard targets such as VIPs, military, and critical national infrastructure, faces heightened risks due to functional gaps and delayed responses at the initial stage of incidents. To overcome these issues, the study proposes a comprehensive risk management framework emphasizing unified command structures, enhanced disaster response integration, clear identification of societal vulnerabilities, and active citizen-led risk management strategies anchored in strategic risk communities. Such an integrated response approach ensures systematic vulnerability management from the earliest phases, reducing potential damage, enhancing community resilience, and effectively preventing complex terrorist incidents from escalating into widespread societal disasters.

https://doi.org/10.12652/Ksce.2025.45.2.0259

김다빈(Kim, Da Been) ; 정인근(Jung, In Keun) ; 공정식(Gong, Jeong Sik)

With the development of domestic industries, the construction of infrastructure has increased, and due to the geographical characteristics of Korea, tunnel construction has also risen. Tunnel collapses are difficult to predict and respond to, leading to unavoidable reinforcement work, increased construction difficulty, and potentially leading to worker casualties. Various measures have been implemented for structural monitoring and accident prevention in tunnels. However, the development of sensor-based collapse monitoring technologies and real-time hazard warning systems remains insufficient. This study developed an intelligent sensor that performs multiple functions for tunnel collapse detection and deformation of tunnel shape, including micro-vibration measurement and FFT analysis of tunnel structures. To detect and classify precise collapse risk data within the tunnel, field tests were conducted to collect and analyze the data. To proactively prevent disasters and ensure worker safety, a real-time data measurement and analysis system, along with a risk warning algorithm for detecting danger signals, were developed and implemented in the intelligent sensor firmware. Through the development of such intelligent sensors, it is anticipated that real-time monitoring of on-site hazardous situations and the provision of decision-making data will strengthen the capabilities of construction safety management.

https://doi.org/10.12652/Ksce.2025.45.2.0265

강형석(Kang, Hyeongseok) ; 이기림(Lee, Kirim) ; 신현길(Shin, Hyeongil) ; 김정옥(Kim, Jungok) ; 이원희(Lee, Wonhee)

In this study, we addressed the limitations of conventional earthwork volume estimation methods caused by seasonal and spatial restrictions due to vegetation and the labor-intensive process of artificial vegetation removal. To overcome these challenges, a GNSS(Global Navigation Satellite System) and UAV(Unmanned Aerial Vehicle) equipped with a LiDAR(Light Detection and Ranging) sensor were used to acquire point cloud data. The Reflectance Intensity feature of the data was utilized to generate vegetation and ground-separated datasets. These datasets were then employed to estimate earthwork volume, which was compared and evaluated against the widely used GNSS-based VRS(Virtual Reference Station) surveying method, a standard in civil engineering and research applications This study implemented unsupervised learning algorithms, including K-Means, K-Medoids, and DBSCAN(Density-Based Spatial Clustering of Applications with Noise), to filter reflectance intensity data based on its unique density characteristics and surface textures. The clustering results identified distinct core clusters, and multiple reflections enabled the inclusion of ground points beneath vegetation. The cluster with the highest point density was classified as ground and extracted for further analysis. The ground data, filtered using these clustering algorithms, were employed to calculate total earthwork volume, combining cut and fill volumes. The results indicated overestimations of 1.4 % and 0.3 % and an underestimation of 0.5 %. A weighted accuracy analysis, considering the proportions of cut and fill volumes, confirmed that the K-Means clustering algorithm achieved the highest accuracy among the methods. This research demonstrates the potential of UAV-based LiDAR data and unsupervised learning algorithms to enhance the accuracy and efficiency of earthwork volume estimation, offering a robust alternative to traditional methods while overcoming seasonal limitations and operational inefficiencies.

https://doi.org/10.12652/Ksce.2025.45.2.0277

이재영(Lee, Jae Young) ; 김수민(Kim, Soo Min) ; 홍성철(Hong, Sung Chul)

Various types of patrol robots have been introduced to overcome the limitations of indoor security and crime prevention caused by shortages of security personnel and blind spots in CCTV systems. The optical images taken by patrol robot support remote operators in controlling the robot, understanding the patrol area, and making decisions. When combined with deep learning-based object detection methods, these images enable the rapid and accurate identification of interest objects. Particularly, human detection by patrol robots is an essential function for intruder detection, safety monitoring, and incident detection. However, patrol images captured in low-light indoor environments during nighttime are dark and noisy, making it difficult for patrol robots to perform their tasks effectively. In this study, low-light image enhancement methods (GLADNet, KinD, TBEFN, LLFormer, EnlightenGAN, Zero-DCE) were applied to nighttime indoor patrol images to analyze their effectiveness in improving visibility and enhancing human detection performance using YOLOv8n-seg model. The results showed that the color and brightness of low-light images were effectively restored in the KinD, TBEFN, and LLFormer images, leading to improved visibility and significantly enhanced image quality metrics. Also, the human detection accuracy of the YOLOv8n-seg model increased in the order of KinD and LLFormer images. KinD enhanced the real-time visibility of nighttime patrol images and significantly improved human detection performance. These findings are expected to increase the performance of night patrol robot using optical cameras.