Moon-Soo Song

Long-term deformation of urban road networks can compromise infrastructure performance and requires spatially continuous methods for identifying segments that warrant targeted monitoring. This study integrates Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR), geotechnical data, multivariate k-means clustering, and GIS road-network analysis to evaluate roadbed deformation susceptibility in Daegu, South Korea. A total of 184 ascending Sentinel-1 SLC images acquired between June 2018 and December 2025 yielded 32,590 roadbed persistent scatterers (PS) and 247,734 surrounding-ground PS. Because a single ascending geometry was used, all measurements represent relative line-of-sight (LOS) motion rather than absolute vertical displacement. Roadbed relative LOS velocities ranged from −8.63 to + 4.87 mm/yr. Roadbed and surrounding-ground velocities correlated moderately within a 200 m neighborhood (r = 0.59), while percentile- and exceedance-based comparisons showed slightly heavier deformation tails on the roadbed. Susceptibility was assessed by clustering relative LOS velocity, SPT-derived V_S30, and effective soil depth for 31,263 PS with complete data. The three resulting classes were defined by combinations of deformation and subsurface conditions rather than by velocity thresholds. The critical class exhibited the most negative mean relative LOS velocity (−0.60 mm/yr), lower mean V_S30 (382 m/s), and relatively deep soil conditions, whereas the concern and stable classes had similar mean LOS velocities but were differentiated primarily by their subsurface profiles. Of the approximately 883.7 km major road network, 668.8 km was classified, comprising 161.8 km (24.2%) critical, 284.1 km (42.5%) concern, and 222.8 km (33.3%) stable roads, while 215.0 km remained unclassified because of insufficient PS coverage. Comparison with 19 documented ground-subsidence incidents yielded critical-class enrichment ratios of 0.34–0.49, but the limited sample was insufficient to either confirm or reject the susceptibility classification; 14 of the 19 incidents were associated with abrupt underground-utility or construction-related failures rather than gradual deformation processes. The proposed framework can support targeted monitoring, preventive maintenance, and sustainable urban road management.

This research focuses on developing a deep learning-based framework to forecast maintenance expenditures within the residential sector. To maintain building value, resident safety, and energy efficiency, consistent facility maintenance is indispensable. This necessity is especially heightened given the recent increase in the construction of supertall and high-performance buildings. However, estimating repair costs for residential facilities is challenging due to the diverse building types, ownership structures, and occupancy patterns compared to other property uses. Therefore, this research proposes a deep-learning model to establish a highly reliable and scientific method for estimating repair costs using empirical data gathered from actual residential facilities. Among the deep learning algorithms, Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) were adopted to develop models and optimize them through a fixed split. The framework and results of this paper facilitate the prediction of maintenance costs for residential facilities, which can contribute to budget planning, long-term facility management, preventive maintenance, resource management, and advanced decision-making. Moreover, it will contribute to the advancement of facility management of residential facilities.

High-rise buildings present substantial challenges for firefighting and rescue operations owing to their considerable height. The stack effect, which becomes more pronounced with increasing building height, accelerates smoke propagation and significantly increases the likelihood of casualties. This study identifies and analyzes the risks associated with fire incidents in high-rise residential buildings. A 49-story building was selected as the reference model, and population density was applied to estimate occupant numbers for the risk assessment. For the damage scenario, one disaster-vulnerable individual per household was assumed. The simulation results revealed that firefighters and vulnerable occupants were exposed to smoke within 541 s. The findings of this study indicate that the stack effect, amplified by building height, exacerbates fire and smoke spread, thereby increasing firefighting risks and potential casualties. These results highlight fire incidents in high-rise structures as a critical category of urban disaster. Furthermore, the study underscores the limitations of existing firefighting facilities in addressing such scenarios and emphasizes the urgent need for new paradigms in firefighting strategies and smoke control technologies to mitigate the risks associated with the stack effect.

Ground settlement is a multifaceted geological phenomenon driven by natural and man-made forces, posing a significant impediment to sustainable urban development. Thus, ground settlement susceptibility (GSS) mapping has emerged as a critical tool for understanding and mitigating cascading hazards in seismically active and anthropogenically modified sedimentary basins. Here, we develop an integrated framework for assessing GSS in the Pohang region, South Korea, by integrating Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR)-derived vertical land motion (VLM) data with seismological, geotechnical, and topographic parameters (i.e., peak ground acceleration (PGA), effective shear-wave velocity (V_s³⁰), site period (T_s), general amplification factor (A_F), seismic vulnerability index (K_g), soil depth, topographic slope, and landform classes) through ensemble machine learning models such as Random Forest (RF), XGBoost, and Decision Tree (DT). Analysis of 56 Sentinel-1 SLC images (2017–2023) revealed persistent subsidence concentrated in Quaternary alluvium, reclaimed coastal plains, and basin-fill deposits. Among the tested models, RF achieved the best performance and strongly agreed with field evidence of sand boils, liquefaction, and structural damage from the 2017 Pohang earthquake. The very-high-susceptibility zones exhibited mean subsidence rates of −3.21 mm/year, primarily within soft sediments (V_s³⁰ < 360 m/s) and areas of thick alluvium deposits. Integration of the optimal RF-based GSS index with regional building inventories revealed that nearly 65% of existing buildings fell within high- to very-high-susceptibility zones. The proposed framework demonstrates that integrating PSInSAR and ensemble learning provides a robust and transferable approach for quantifying ground settlement hazards and supporting risk-informed urban planning in seismically active and complex geological coastal environments.

Reclaimed coastal areas are highly susceptible to uneven subsidence caused by the consolidation of soft marine deposits, which can induce differential settlement, structural deterioration, and systemic risks to urban infrastructure. Further, engineering activities, such as construction and loadings, exacerbate subsidence, impacting infrastructure stability. Therefore, monitoring the integrity and vulnerability of linear urban infrastructure after construction on reclaimed land is critical for understanding settlement dynamics, ensuring safe and reliable operation and minimizing cascading hazards. Subsequently, in the present study, to monitor deformation of the linear infrastructure constructed over decades-old reclaimed land in Mokpo city, South Korea (where 70% of urban and port infrastructure is built on reclaimed land), we analyzed 79 Sentinel-1A SLC ascending-orbit datasets (2017–2023) using the Persistent Scatterer Interferometry (PSInSAR) technique to quantify vertical land motion (VLM). Results reveal settlement rates ranging from −12.36 to 4.44 mm/year, with an average of −1.50 mm/year across 1869 persistent scatterers located along major roads and railways. To interpret the underlying causes of this deformation, Casagrande plasticity analysis of subsurface materials revealed that deep marine clays beneath the reclaimed zones have low permeability and high compressibility, leading to slow pore-pressure dissipation and prolonged consolidation under sustained loading. This geotechnical behavior accounts for the persistent and spatially variable subsidence observed through PSInSAR. Spatial pattern analysis using Anselin Local Moran’s I further identified statistically significant clusters and outliers of VLM, delineating critical infrastructure segments where concentrated settlement poses heightened risks to transportation stability. A hyperbolic settlement model was also applied to anticipate nonlinear consolidation trends at vulnerable sites, predicting persistent subsidence through 2030. Proxy-based validation, integrating long-term groundwater variations, lithostratigraphy, effective shear-wave velocity (V_s³⁰), and geomorphological conditions, exhibited the reliability of the InSAR-derived deformation fields. The findings highlight that Mokpo’s decades-old reclamation fills remain geotechnically unstable, highlighting the urgent need for proactive monitoring, targeted soil improvement, structural reinforcement, and integrated InSAR-GNSS monitoring frameworks to ensure the structural integrity of road and railway infrastructure and to support sustainable urban development in reclaimed coastal cities worldwide.

Owing to climate change, abnormal climate phenomena occur frequently, damaging aging highway facilities. Therefore, it is necessary to develop adaptation projects for highway systems. Adaptation projects focus on mitigating the impact of climate change on highway facilities and enabling safe use. The present study aims to develop quantified evaluation indicators that are necessary for planning and selecting appropriate climate change adaptation projects. By analysing previous studies, the most important factors to be considered in the evaluation process of climate change adaptation projects were defined, and 20 preliminary evaluation indicators were divided into three layers. A feasibility study for each class was performed on the preliminary evaluation indicators by an expert panel; 16 evaluation indicators were selected through a feasibility study, and the weight of each indicator was calculated using the analytical hierarchy process (AHP) method. The priority of the evaluation indicators was service improvement through adaptation projects (weight 8.76), cost reduction and job creation (weight 8.46), and climate change impact reduction and vulnerability reduction (weight 8.31). These quantified evaluation indicators can help organizations that manage highways respond to climate change and establish adaptation projects.

The inner shelf and coastal region of the Yellow Sea along the Korean peninsula are frequently impacted by Typhoons. The Mokpo coastal region in South Korea has been significantly affected by typhoon Soulik in 2018, the deadliest typhoon strike to the southwestern coast, since Maemi in 2003. Typhoon Soulik overran the region, causing extensive damage to the coast, shoreline, vegetation, and coastal geomorphology. Therefore, it is important to investigate its impact on the coastal ecology, landform, erosion/accretion, suspended sediment concentration (SSC) and associated coastal changes along the Mokpo region. In this study, net shoreline movement (NSM), Normalized Difference Vegetation Index (NDVI)), coastal landform change model, Normalized Difference Suspended Sediment Index (NDSSI), and SSC-reflectance relation have been used to analyze the coastal morphodynamics over the typhoon periods. We used pre- and post-typhoon Sentinel-2B MSI images for mapping and monitoring the typhoon effect. The findings highlighted the significant impacts of typhoons on coastal dynamics, wetland vegetation and sediment resuspension along the Mokpo coast. It has been observed that typhoon-induced SSC influences shoreline and coastal morphology. The outcome of this research may provide databases to manage coastal environments and a long-term plan to restore valuable coastal habitats. In addition, the findings may be useful for post-typhoon emergency response, coastal planners, and administrators involved in the long-term development of human life.