Abstract
Preface:
This special issue (Part I & Part II) of Steel and Composite Structures: An International Journal
presents selected contributions under the theme of forensic research on steel/composite structures
and geo/hydro systems. The objective of this issue is to highlight advanced techniques and
innovative approaches for understanding structural performance, deterioration, and failure
mechanisms across a wide range of infrastructures.
The papers cover experimental investigations, numerical modeling, and analytical studies,
reflecting both established methods and emerging technologies. Topics include structural
evaluation of steel and composite systems, geotechnical and hydraulic system failure analysis,
InSAR-based ground monitoring, material behavior under extreme conditions, and data-driven
applications such as machine learning in civil and forensic engineering.
By bringing together these diverse studies, the special issue (Part I & Part II) provides a platform
for sharing new insights and solutions that contribute to safer and more resilient infrastructure. We
hope that the findings and discussions presented here will stimulate further research and broaden
the scope of forensic engineering in steel/composite structures and geo/hydro systems.
Key Words
Address
Seungjun Kim: Korea University, South Korea
Yong-Hoon Byun: Kyungpook National University, South Korea
Abstract
This study evaluates the applicability of ground penetrating radar (GPR) and time domain reflectometry (TDR) for
assessing rutting depth on unpaved roads. A testbed is constructed using sandy soil, and vehicular loading is applied through
repeated passes of a dump truck (0, 100, 200, and 500 times). The rutting depth is estimated based on the travel time extracted
from the GPR images and permittivity values obtained from the TDR measurements. The accuracy of these estimates is
compared with the results of dynamic cone penetrometer (DCP) tests. Additionally, the rutting depth estimated by GPR analysis
is compared with the values obtained using the conventional straightedge method. The results indicate that incorporating GPR
with TDR enables accurate estimation of subsurface interface depths. Moreover, while conventional methods tend to
underestimate the rutting depth owing to the deformation of the reference surface, the GPR-based analysis shows minimal error
from such deformation. This study demonstrates that GPR and TDR can be powerful tools for evaluating the rutting depth across
an entire road cross section.
Key Words
ground penetrating radar; relative permittivity; rutting depth; time domain reflectometry; unpaved road
Address
Seonghun Kang: School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Korea
Geunwoo Park: School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Korea
Seungjun Kim:School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Korea
Thomas H.-K. Kang: Department of Architecture & Architectural Engineering, Seoul National University, Seoul 08826, Korea
Jong-Sub Lee: School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Korea
Abstract
This study investigates the failure behavior of earth dams in response to various shear strength parameters using a
two-phase material point method (MPM). A total of 20 numerical dam models are developed by systematically varying the
cohesion and internal friction angle of the embankment fill material. The failure evolution and displacement distributions of
these models are analyzed using the two-phase MPM, while the factor of safety (FS) is determined using the finite element
method. Based on the numerical results, lower cohesion and friction angles lead to more pronounced displacements and lower
FS values. For sand-filled dam models, the displacement and FS are both highly sensitive to changes in the strength parameters.
Conversely, clay-filled dam models exhibit minimal displacement despite displaying significant reductions in FS to values near
unity. Therefore, the response of earth dams to strength parameters depends strongly on the soil type, and interpreting the
displacement and FS together can facilitate reliable stability evaluation.
Key Words
earth dam; material point method; factor of safety; large-deformation analysis; shear strength parameters
Address
Juik Son:Department of Agricultural Civil Engineering, Kyungpook National University,
80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
Dong-Ju Kim: School of Civil, Environmental and Architectural Engineering, Korea University,
145, Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
Hyungjoon Seo:Department of Civil Engineering, Seoul National University of Science and Technology,
232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of Korea
Yong-Hoon Byun:Department of Agricultural Civil Engineering, Kyungpook National University,
80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
Abstract
There is a lack of quantitative constraints on the mechanism of groundwater fluctuations on the settlement of urban
rail transit appurtenant structures. In this study, four air vents along the Bundang Line in Seoul, South Korea, were selected to
analyze 168 Sentinel-1A C-band images acquired between July 2018 and January 2025. Using the SBAS-InSAR (Small
Baseline Subset Interferometric Synthetic Aperture Radar) technique, a deformation time series covering the entire line was
derived. Based on the SBAS-InSAR results, the maximum settlement rate of Bundang Line is -4.4 mm/year, and the maximum
cumulative settlement is about 20 mm. Subsequently, the results of 3 wavelet transform tools to quantify the seasonal
deformation of Air Vent and the underground water level show that the change of the underground water level is the primary
external driver of the seasonal deformation of Air Vents, and part of the air vent deformation lags the underground water level by
about 20 days. In this study, underground water level fluctuation is identified as the main driver of settlement in the Air Vent of
Bundang Line, and response lags of 20-45 d are given for each section. The combination of SBAS-InSAR time series based on
Sentinel-1A imagery and wavelet analysis can capture this hydrologic-deformation linkage in time with millimeter-level
accuracy and monthly time delay, providing reliable data support for early warning of ground settlement along the subway
tunnel and decision making for disaster mitigation.
Abstract
This study numerically evaluates the behavior of timber scarf joints with and without steel pins using finite element
models across various parameters. Three-dimensional (3D) nonlinear finite element models were developed based on a micro
modeling approach to examine their response to static loads. Experimental results were used to calibrate and validate the
numerical models. Experimental results were used to calibrate and validate the numerical models. Sensitivity analysis revealed
that the material properties of the scarf members were the most critical factor in achieving strong connections, surpassing the
influence of key or dowel members. Simulation accuracy improved with smaller mesh sizes, increased load increments, and
more iterations, though at the expense of higher complexity and longer computation times. Parametric analysis provided deeper
insights into scarf joint behavior under tension and compression loads parallel to the grain. Analysis on the geometric parameter
revealed that the basic size proportions recommended in the literature provide the best combination, offering greater strength
compared to other size combinations. Additionally, timber scarf joints were analyzed with varying numbers of steel or wooden
dowels, focusing on identifying the impact of the dowel to scarf joints. This study highlights the potential of using numerical
modeling to evaluate scarf joints, while acknowledging the limitations of the tools.
Key Words
finite element model; parametric analysis; scarf joint; steel pin; traditional timber connection
Address
Hafshah Salamah: Department of Architecture, Institute of Technology Bandung, Ganeca 10 Bandung, Indonesia
Goangseup Zi: Department of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea
Jong-Sub Lee: Department of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea
Thomas H.-K. Kang: Department of Architecture and Architectural Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Abstract
This study proposes and validates a load and resistance factor design (LRFD)-based approach for 345 kV
transmission towers in South Korea. A new tower was designed following LRFD principles and compared to an existing tower
developed using the conventional allowable stress design (ASD) method. The LRFD-based design achieved an approximate
11% reduction in structural weight by optimizing member sizing. Structural vulnerabilities and nonlinear failure behaviors under
critical load combinations were first identified through finite element analysis and subsequently verified via full-scale load
testing. The experimental program assessed structural performance under four major LRFD load combinations and
benchmarked the results against those derived from ASD conditions. Additionally, the ultimate strength and failure
characteristics were investigated under extreme wind loading with all conductors intact. Despite the reduced member sizes, the
LRFD-designed tower satisfied all load-bearing requirements. The failure test, conducted by incrementally increasing the
applied load, revealed a buckling failure at 135% of the design load, closely aligning with the nonlinear analysis predictions.
These findings confirm that the proposed LRFD-based approach ensures both structural reliability and material efficiency,
offering a valuable reference for the optimization and enhancement of future transmission tower designs.
Abstract
This paper presents a new approach for predicting and understanding the fire response of concrete filled steel tubes
(CFSTs) using explainable and symbolic machine learning. By leveraging unsupervised and supervised learning techniques, we
develop a number of ML models to understand the fire response of CFSTs, predict their failure modes, mechanical and thermal
responses, derive new design equations for CFST behavior under fire, and optimize the design of such columns for improved
fire resistance. Our methodology includes clustering for identifying structural performance patterns, regression and classification
models for failure prediction, and symbolic regression for generating interpretable models that offer insights into the underlying
mechanics. More specifically, the clustering analysis revealed three distinct structural performance patterns among the CFST
columns (namely, those governed by the material strength, the geometric properties of the tube, as well as a combination of the
magnitude of the loading conditions and boundary conditions). Further, regression and classification models were developed for
failure prediction, achieving an accuracy of 88% in predicting buckling and crushing failure modes. Extensive evaluation against
existing standards reveals our approach's advantages in accuracy and predictability, with the CatBoost model predicting rebar
and core temperature with an accuracy of 95%. This work presents a significant step toward enhancing fire-resistant design
through ML-driven discovery, thereby improving fire safety and performance.
Key Words
composite columns; concrete filled steel tubes; fire resistance; machine learning
Address
V.K. Kodur: 1) Department of Civil and Environmental Engineering, Michigan State University, USA
2) Architectural and Urban Systems Engineering, Ewha Womans University, Seoul, South Korea
M.Z. Naser: 1) School of Civil & Environmental Engineering and Earth Sciences (SCEEES), Clemson University, USA
2) Artificial Intelligence Research Institute for Science and Engineering (AIRISE), Clemson University, USA
Hee Sun Kim: Architectural and Urban Systems Engineering, Ewha Womans University, Seoul, South Korea
Abstract
Machine learning (ML) has been increasingly adopted across various disciplines, including civil engineering (CE),
to address a wide range of complex problems. This study conducts a systematic literature review to examine recent trends in the
ML applications within CE and to identify key challenges associated with its implementation. The review is proposed focusing
on four research questions concerning data scarcity, efficient construction of learning datasets, overfitting mitigation, and the
integration of CE's multidisciplinary nature. The analysis focuses on five major fields in CE— structural, geotechnical,
transportation, water and environmental, and energy engineering—and evaluates the application of five prominent ML
techniques: multilayer perceptron, convolutional neural network, recurrent neural network, generative adversarial network, and
reinforcement learning. A total of 800 ML studies in CE were reviewed. Key subfields within each CE domain were identified,
and domain-specific applications of ML were synthesized to address the predefined research questions. The findings of this
study provide practical insights and methodological guidance for researchers aiming to apply ML to real-world CE challenges in
a robust and informed manner.
Key Words
civil engineering; machine learning; systematic literature review
Address
Jae-Hyun Kim: Department of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Republic of Korea
Sanghoon Jun: Department of Civil Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
Donghwi Jung: Department of Civil Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
Yong-Hoon Byun: Department of Agricultural Civil Engineering, Kyungpook National University,
80 Daehak-ro, Buk-gu, Daegu, 41566, Republic of Korea
Seungjun Kim: School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Republic of Korea
Chulsang Yoo: School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Republic of Korea
open access
