Abstract
This paper presents a parametric analysis of a two-conductor transmission line embedded in steel–concrete
composite structures. The line configuration consists of a steel rebar and PVC-insulated copper wire, with spacing varied from
near-contact (14.35 mm) to 3 m. The resistance, inductance, capacitance, conductance, and characteristic impedance of the
transmission line are modeled considering skin effect, proximity effect, and frequency. The results show that resistance and
conductance are strongly affected by both frequency and conductor spacing, whereas inductance and capacitance are primarily
governed by geometry. Characteristic impedance exhibits a combined dependence on spacing and frequency, with significant
increases observed at wide spacings and high frequencies. These findings provide valuable insights into the electromagnetic
behavior of embedded transmission lines, with implications for structural health monitoring of steel–concrete composite
structures.
Key Words
characteristic impedance; frequency-dependent RLGC; skin and proximity effects; steel–concrete
composite; transmission line
Address
Dongsoo Lee:Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, 205 North Mathews, Urbana, IL 61801, USA
Jong-Sub Lee:School of Civil, Environmental and Architectural Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
Younghoon Lee:School of Civil, Environmental and Architectural Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
Thomas H.-K. Kang:Department of Architecture & Architectural Engineering, Seoul National University,
1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
Jung-Doung Yu:Department of Civil Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do, Republic of Korea
Abstract
Basic Oxygen Furnace (BOF) steel slag, a major byproduct of the steel industry, has been widely recycled for
various engineering applications. Before commercialization and reuse, steel slag undergoes an aging process that involves
spraying water over the stacked slag for several months in open yards to mitigate the unforeseen environmental risk. This study
experimentally investigates the behavior of steel slag upon exposure to water, with particular focus on the characteristics of
leachate, which are closely associated with the aging process. Two steel slag types were prepared: raw materials and those
pressurized with CO₂ to promote carbonate formation via reaction with Ca²⁺. Both were packed into the column and leached
with either distilled water or seawater at different flow rates. Effluent pH and turbidity were monitored over time, Also, the
leachates were stirred in ambient air to allow further carbonation. Solid residues were analyzed using Scanning Electron
Microscope (SEM), X-ray Energy-Dispersive Spectroscopy (EDS), and Thermogravimetric Analysis (TGA). Pressurizing steel
slag with CO₂ was found to reduce leachate pH, while turbidity was primarily attributed to suspended carbonate particles. Flow
rate had a negligible effect, whereas seawater interaction induced a turbidity rise.
Key Words
BOF steel slag; Alkalinity; Turbidity; CO₂ pressurized carbonation
Address
Jeehoon Ma:School of Civil and Environmental Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
Won Hee Lee:School of Civil and Environmental Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
Yong-Hoon Byun:Department of Agricultural Civil Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu, 41566, Republic of Korea
Tae Sup Yun:School of Civil and Environmental Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
Abstract
Sinkhole events are becoming more frequent in urban areas, making reliable ground monitoring essential. Ground
displacement patterns around four sinkhole sites in Seoul were examined using Sentinel-1 SAR (Synthetic Aperture Radar) data
collected from 2018 to 2025. The SBAS-InSAR (Small Baseline Subset Interferometric Synthetic Aperture Radar) technique
was used to detect long-term subsidence trends. At each site, displacement data were compared between points close to the
sinkholes and points farther away. To improve the detection of unusual surface changes, a method combining WT (Wavelet
Transform) and SBAS-InSAR was developed, referred to as WT-SBAS-InSAR. Wavelet transform was applied to the original
InSAR time series to identify localized frequency changes. These changes appeared near the time of known sinkhole events.
InSAR data from distant control points did not show similar frequency increases. The results suggest that satellite-based
interferometric methods, especially when combined with time-frequency analysis such as wavelet transform, can help detect
early signs of sinkhole formation. These findings also indicate potential for future use in predictive modeling to improve urban
infrastructure safety.
Key Words
complex networks; mathematical simulation; mechanical behavior; nanotechnology
Address
Naeryoung Choi:Department of Civil Engineering, Seoul National University of Science and Technology, SEOUL, 01811, Korea
Lang Fu:Department of Civil and Environmental Engineering, University of Liverpool, UK L69 7ZX, UK
Jong-Sub Lee:School of Civil, Environmental and Architectural Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Korea
Hyungjoon Seo:Department of Civil Engineering, Seoul National University of Science and Technology, SEOUL, 01811, Korea
Abstract
Traditional methods for classifying micromechanical properties in alkali-activated materials depend on manual
correlation of nanoindentation data, which is both time-consuming and subjective. This study examines the application of
unsupervised machine learning to automate phase identification in alkali-activated glass powder and blast furnace slag. Grid
nanoindentation was combined with scanning electron microscopy and energy dispersive X-ray spectroscopy to uncover
heterogeneous phase assemblages. A Gaussian mixture model (GMM) was utilized to distinguish among the outer matrices,
particles, rims, and their respective proportions. The GMM-based results were compared with those obtained through manual
classification. The optimal number of clusters was determined using the Bayesian information criterion. Accuracy was assessed
based on phase prediction error and normalized center prediction error. The tied covariance model with eight clusters showed the
highest agreement with manually classified phases, which minimizes centroid and phase fraction errors. This approach enables
robust, quantitative evaluation of micromechanical properties in glass-based phases, significantly reducing the need for manual
classification.
Address
Seunghoon Seo:School of Civil, Environmental and Architectural Engineering, Korea University, 145
Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
Yujin Lee:Department of Structural Engineering Research, Korea Institute of Civil Engineering
and Building Technology, 283, Goyangdae-Ro, Ilsanseo-Gu, Goyang-Si, Gyeonggi-Do, 10223, Republic of Korea
Young K. Ju:School of Civil, Environmental and Architectural Engineering, Korea University, 145
Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
Ilhwan You:Department of Rural Construction Engineering, Jeonbuk National University, 567
Baekje-daero, Deokjin-gu, Jeonju-si, 54896, Jeollabuk-do, Republic of Korea
Goangseup Zi:School of Civil, Environmental and Architectural Engineering, Korea University, 145
Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
Abstract
Point cloud segmentation is crucial for Forensic Information Modeling (FIM) and Building Information Modeling
(BIM) applications; however, existing methods either require extensive training data (deep learning) or struggle in complex
architectural layouts (geometry-based). This paper presents a door-guided geometric framework that achieves robust indoor
space segmentation without learned features. The approach introduces four preprocessing modules: 1) door gap detection
through cross-sectional analysis (requiring only 4 manual clicks to identify all doors in a building), 2) corridor isolation via
principal component analysis, 3) tile-based structural filtering, and 4) verticality-based wall extraction. These modules establish
spatial boundaries before applying hierarchical watershed segmentation with multi-scale spillage prevention. Validated on the
S3DIS Area 6 dataset (27 rooms, 1.17 million points), the framework achieved an average IoU of 96.5% and an F1-score of
98.0%, matching deep learning performance while eliminating training requirements. The purely geometric approach enables
deployment in forensic engineering contexts where training data is unavailable and computational resources are limited, directly
supporting damage assessment and structural investigation workflows.
Key Words
door-guided framework; geometric segmentation; indoor segmentation; watershed algorithm
Address
Seung H. Song:Department of Civil, Environmental and Architectural Engineering, Korea University, Seongbuk-Gu, Seoul 02841, Republic of Korea
Seokju Shin:Department of Civil, Environmental and Architectural Engineering, Korea University, Seongbuk-Gu, Seoul 02841, Republic of Korea
Changsu Lee:Department of Civil and Environmental Engineering, University of Alberta, 9211-116 Street, Edmonton, Alberta T6G 2H5, Canada
Heejae Ahn:Department of Civil and Environmental Engineering, University of Alberta, 9211-116 Street, Edmonton, Alberta T6G 2H5, Canada
Seungjun Kim:Department of Civil, Environmental and Architectural Engineering, Korea University, Seongbuk-Gu, Seoul 02841, Republic of Korea
Hunhee Cho:Department of Civil, Environmental and Architectural Engineering, Korea University, Seongbuk-Gu, Seoul 02841, Republic of Korea
Abstract
This study evaluates the role of additional constant water resources to the variable water resources. The variable
water resources indicate dam reservoirs, whereas the constant water resources indicate desalination. This study focuses on the
water supply system in the Han River basin, Korea, particularly on the increasing failure frequency of water supply. As a first
step, this study evaluates the changing trends in water supply failures over the last 50 years. Frequency analysis of both shortage
frequency and shortage volume is conducted. Also, the same analysis is repeated while considering the addition of constant
water resources. The difference between the two is then quantified as the effect of additional constant water resources. The
results are summarized as follows. Frequency analyses result show that the water shortage problem has significantly worsened
recently. For example, the occurrence probability of 20 or more water shortages over the 30-year period was almost zero in the
1980s; however, it has now approached one. Similarly, the return period for the shortage ratio of 0.2 was more than 400 years in
the 1980s; but has now decreased to less than 30 years. The effect of additional constant water resources is found to be
significant. With an additional constant water resources of 1.0 x 106 m³/day, the occurrence probability of 20 or more water
shortages decreases from nearly 1.0 to around 0.6, which can also increase the return period for the shortage ratio of 0.1 from 10
years to about 20 years.
Key Words
Beta distribution; constant water resources; failure in water supply; frequency analysis; Poisson distribution;
variable water resources
Address
Chulsang Yoo:School of Civil, Environmental and Architectural Engineering, College of Engineering, Korea University, Seoul 02841, Korea
Jeong-Hyeok Ma:School of Civil, Environmental and Architectural Engineering, College of Engineering, Korea University, Seoul 02841, Korea
Eunho Ha:Division of Data Science, Yonsei University, Wonju 26493, Korea
Changhyun Jun:School of Civil, Environmental and Architectural Engineering, College of Engineering, Korea University, Seoul 02841, Korea
Abstract
This study proposes a convolutional neural networks (CNNs)-based framework for estimating rainfall intensity
using acoustic signals acquired from raindrops. Raindrop sounds were collected under real-world conditions using an internet of
things (IoT) sensor-based acoustic data collection device. The collected signals were then transformed into spectrotemporal
representations via short-time Fourier transform (STFT) and mel-spectrogram analysis. A dual-stream CNNs model was
constructed to learn from both spectrogram types, leveraging their complementary strengths in capturing high- and low
frequency signal characteristics across various rainfall intensities. The model was trained using a balanced dataset representing
no rain, weak, moderate, and heavy rainfall, and validated against ground truth measurements from an optical disdrometer (i.e.,
OTT PARSIVEL²). Evaluation results indicate that the proposed method yields promising performance, with a root mean square
error of 4.89 mm/h, a mean absolute error of 2.02 mm/h, and a R² of 0.75. While the model effectively estimates weak to
moderate rainfall, it tends to underestimate extreme rainfall events due to their underrepresentation in the training data. These
findings demonstrate the feasibility of rainfall intensity estimation from acoustic signals and highlight the potential of deep
learning-based acoustic sensing for hydrometeorological applications in observation-challenged areas.
Address
Seunghyun Hwang:Department of Civil, Environmental and Architectural Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, Republic of Korea
Jinwook Lee:Department of Civil and Environmental Engineering, University of Hawaii at Manoa, 2500 Campus Road, Honolulu, HI 96822, USA
Carlo De Michele:Department of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milano, Italy
Jongyun Byun:Department of Civil, Environmental and Architectural Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, Republic of Korea
Donghwi Jung:School of Civil, Environmental and Architectural Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, Republic of Korea
Changhyun Jun:School of Civil, Environmental and Architectural Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, Republic of Korea
Abstract
Earthquakes pose substantial structural damage to infrastructural facilities across the globe, particularly in
developing countries. This paper focuses on establishing the structural vulnerability of reinforced concrete (RC) school buildings
in Kashmir region of Pakistan. Vulnerability assessment plays a substantial role in identifying risk-prone structures and
prioritizing structural intervention efforts. Statistical analyses for a database of school buildings were conducted to develop the
statistical distributions of important structural parameters including total structural heights, number of bays, and cross-sectional
dimensions of primary structural members. Afterwards, correlation analysis was performed to check for their interdependence.
Acute insights were obtained and subsequently, 3 nonlinear representative models for 1-, 2-, and 3-story schools' representative model was found to be highly susceptible with maximum collapse probability of 0.46. This study is the
first of its kind in Kashmir region that statistically incorporates the information for developing representative analytical school
building models instead of taking a discreet structure to characterize the whole stock. The presented work establishes critical
insights into the features of school buildings' stock in Kashmir and demonstrates the representative models' capability in
predicting structural vulnerability.
Key Words
earthquakes; fragility curves; incremental dynamic analysis; school buildings; vulnerability assessment
Address
Muhammad Zain:Department of Architecture, Faculty of Architecture, Chulalongkorn University, Bangkok, 10330, Thailand
Chi-Tathon Kupwiwat:Department of Architecture, Faculty of Architecture, Chulalongkorn University, Bangkok, 10330, Thailand
Thomas H.-K Kang:Department of Architecture & Architectural Engineering, Seoul National University, 08826, Republic of Korea
Lapyote Prasittisopin:1)Center of Excellence on Green Tech in Architecture, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand
2)Department of Materials Science, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand
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