https://doi.org/10.11112/jksmi.2026.30.2.1

김영환(Younghwan Kim) ; 오홍섭(Hongseob Oh)

This study evaluated the early-age mechanical properties and microstructure of hydrophobic Calcium Aluminate Cement (CAC) mortar composites mixed with a hydrophobic polymer (PDMS) and a cross-linking agent (TEOS) to enhance the durability of concrete structures. To address the surface treatment issues of inherently hydrophilic conventional concrete, Ordinary Portland Cement (OPC) was mixed with CAC cement, which possesses excellent chemical and fire resistance. A hydrophobic solution, prepared by mixing PDMS and TEOS at a weight ratio of 4:1, was then incorporated at 1.5% or 2.0% to produce the hydrophobic mortar. The fundamental properties of the composite were analyzed through flow, length change, water absorption, contact angle, flexural strength, and compressive strength tests. All mortar variables incorporating the hydrophobic solution exhibited sufficient hydrophobicity, with the water contact angle exceeding 120°, and showed a lower primary water absorption rate compared to the OPC variable7. Notably, the CAC_10_1:1.5_P1.5 mix simultaneously displayed low water absorption and an excellent contact angle8888. This mix also showed delayed strength development after 7 days, but ultimately secured a strength similar to that of OPC. Microstructural analysis (SEM) confirmed that Ettringite crystals and a C-A-S-H gel network were densely formed in the hydrophobic mortar.

https://doi.org/10.11112/jksmi.2026.30.2.10

김유빈(Yu-Been Kim) ; 이종한(Jong-Han Lee)

This study proposes an automated method for generating a damage-integrated graphic model by combining UAV-based deep learning damage detection, quantification, and localization. The proposed framework can detect three types of bridge damage― crack, spalling and exposed rebar―using fine-tuned YOLOv11 models. For detected damages, cracks are quantified at the pixel level to measure length and width, while spalling and exposed rebar are quantified to measure length and width based on bounding box dimensions. The detected damages are then localized by establishing correspondences between UAV imagery and the graphic model, allowing damage visualization at their corresponding locations within the model. Compared to conventional BIM-based approaches, which are often complex and computationally heavy, the proposed method leverages lightweight graphic models that provide advantages in intuitive visualization and rapid damage integration. This approach supports efficient bridge condition assessment and maintenance decision-making, particularly when integrated with large-scale UAV datasets.

https://doi.org/10.11112/jksmi.2026.30.2.17

강기상(Gi-Sang Kang) ; 유용래(Yong-Rae Yu) ; 안호준(Hojune Ann) ; 이종재(Jong-Jae Lee)

Conventional pavement marking inspections rely heavily on manual visual assessment, which is inherently subjective, labor-intensive, and inefficient for large-scale road networks. To overcome these limitations, this study proposes an AI-based system for quantitative analysis of deteriorations in road markings by integrating object detection and image processing techniques. The proposed system consists of five stages: data acquisition, preprocessing, object detection, deterioration analysis, and result reporting. Forward-facing road images captured by a vehicle-mounted camera are first transformed into a top-view representation using inverse perspective mapping (IPM) to eliminate geometric distortion and ensure spatial consistency. A YOLOv8-based object detection model is then employed to robustly extract lane marking regions under diverse road conditions. For quantitative defect assessment, boundary refinement using Harris corner detection and pixel-level density analysis is applied to estimate deterioration ratios, including micro-cracks and material peeling within the lane markings.Experimental validation using a small-scale testbed and real-world driving data demonstrated that the proposed system achieved a mean Average Precision (mAP) of 90.3% for lane detection and an average defect analysis accuracy of 86.4% compared to ground-truth measurements. These results confirm the effectiveness and reliability of the proposed system for automated road marking condition assessment.

https://doi.org/10.11112/jksmi.2026.30.2.25

김남현(Nam-Hyun Kim) ; 강승범(Seung-Beom Kang) ; 김선희(Sun-Hee Kim) ; 최원창(Wonchang Choi)

This study analyzes the drying shrinkage characteristics of concrete incorporating biochar as an alternative supplementary cementitious material. To evaluate drying shrinkage performance, free shrinkage tests were conducted for up to 90 days under controlled temperature and relative humidity conditions. The progression and final shrinkage values were analyzed, alongside the properties of fresh concrete and the mechanical properties of hardened concrete, to examine the comprehensive effects of biochar incorporation. The test results demonstrate that biochar-incorporated concrete exhibits reduced shrinkage compared to plain concrete. This reduction is primarily attributed to the internal curing effect of biochar; its porous structure absorbs and stores free water and gradually releases it during hydration and drying, thereby mitigating internal moisture loss within the concrete matrix. Furthermore, microstructural observations reveal that the interfacial transition zone in biochar-incorporated concrete undergoes densification. This densification promotes pore refinement and restricts moisture evaporation pathways, thereby enhancing resistance to shrinkage. Overall, these findings suggest that biochar can be utilized as a sustainable alternative supplementary cementitious material to improve the dimensional stability and durability of concrete.

https://doi.org/10.11112/jksmi.2026.30.2.33

최진우(Jin-Woo Choi) ; 정진희(Jin-Hee Chung) ; 김선희(Sun-Hee Kim)

This study forms part of the development process of a new bridge bearing replacement method intended to overcome the limitations of conventional replacement techniques that require demolition of the existing bearing pedestal. In this research, the strengthening performance of epoxy under horizontal loading was experimentally evaluated when cracks developed in concrete blocks due to aging. Specimens were fabricated by installing reinforcing plates within the concrete blocks to enable installation of bridge bearing structural components, and internal voids were filled using epoxy injection. The experimental variables included the presence of cracks and the number of epoxy injection cycles. Horizontal loads were applied to examine structural behavior and failure modes. The experimental results indicated that the epoxy-repaired bearing pedestals exhibited approximately 1.2% higher strength than the undamaged specimen. Furthermore, complete crack filling was achieved when epoxy injection was performed twice.

https://doi.org/10.11112/jksmi.2026.30.2.41

이승의(Seung Ui Lee) ; 허인욱(Inwook Heo) ; 주예진(Ye Jin Ju) ; 김성배(Sung Bae Kim) ; 김도범(Do Bum Kim) ; 김강수(Kang Su Kim)

This study investigated the structural behavior of steel permanent formwork columns subjected to construction loads and conducted an analytical study to predict maximum out-of-plane deformation. To achieve this, parametric analyses were performed, each setting three levels for five key variables: rib spacing, column band spacing, plate thickness, sectional size, and maximum lateral pressure. The results of finite element analysis (FEA) was then collected to build an extensive behavioral database. Based on the analysis results, the structural contributions of each component were also quantitatively analyzed. These were integrated based on the principle of superposition and a formula for estimating maximum out-of-plane deformation based on Euler-Bernoulli beam theory was proposed. To improve the prediction accuracy of the formula, a numerical optimization technique was applied to derive a reasonable correction factor. The validation results showed that the proposed formula closely matched the analytical values, particularly in the low-deformation section. Since this section is a serviceability review area directly related to the quality of the concrete finish, the maximum out-of-plane deformation formula proposed in this study is considered a useful model that combines structural reliability with practical prediction accuracy.

https://doi.org/10.11112/jksmi.2026.30.2.52

김일순(Il Sun Kim) ; 양은익(Eun Ik Yang)

This study comprehensively analyzed the effects of hyperparameter settings, dataset size, damage-type-specific performance characteristics, and computational efficiency on CNN-based structural damage classification. Experiments were conducted using four CNN models?GoogLeNet, ResNet-50, EfficientNet-B0, and MobileNetV2?and classification performance was quantitatively compared based on test-set accuracy and Macro F1-score. The results demonstrated that, even within the same model, classification accuracy and performance variability varied substantially depending on the hyperparameter configurations. Increasing the training dataset size generally improved overall performance while mitigating sensitivity to hyperparameter variations. However, beyond a certain dataset size, performance gains tended to saturate, and the extent of this saturation differed depending on the model architecture. In addition, under balanced class distributions, performance differences among damage types were minimized. A clear trade-off between classification performance and computational efficiency was observed across the models. This study contributes to the literature by providing an integrated analysis of hyperparameter sensitivity, data efficiency, and computational cost, rather than focusing solely on single optimal performance comparisons.

https://doi.org/10.11112/jksmi.2026.30.2.61

김현식(Hyun-Sik Kim) ; 이원훈(Won-Hun Lee) ; 이강석(Kang-Seok Lee)

It is critically important to investigate how the seismic performance of reinforced concrete members damaged by reinforcement steel corrosion affects the whole seismic capacity of structures. Based on such studies, the seismic capacity of reinforced concrete structures containing corroded reinforcement members, including beams, walls and columns, can be evaluated more accurately. However, existing seismic evaluation guidelines for reinforced concrete structures, including FEMA 310 in the United States and the Japanese standards for seismic capacity evaluation of reinforced concrete structures, do not account for deterioration effects, including reinforcement steel corrosion, in evaluating the seismic capacity of reinforced concrete members. Furthermore, even though previous analytical and experimental researches on corroded reinforced concrete structures have stated quantitative deterioration in strength-deformation capacity, there has been very limited research on the hysteretic behavior of such members based on their strength and deformation capacity resulting from the corroded reinforcement. In conclusion, the seismic capacity of reinforced concrete members with corrosion damages should be must be quantitatively by considering the deterioration in seismic capacity based on load?displacement performance, i.e., hysteretic behavior. The ultimate objective of this study is to propose a practical methodology for evaluating the seismic performance of R/C structures containing corroded members, specifically through a hysteretic model based on load?displacement characteristics at the crack, yield, and ultimate states of corrosion members. As a first step, to investigate the influences of reinforcement steel corrosion on the load-displacement behavior of reinforced concrete beams, beams controlled by shear and flexure were designed and fabricated with varying corrosion levels using an accelerated corrosion method based on impressed current. Finally, monotonic loading tests were conducted to evaluate the load?displacement characteristics at the crack, yield, and ultimate states of the corroded beams controlled by shear and flexure.

https://doi.org/10.11112/jksmi.2026.30.2.73

조인수(In-Soo Cho) ; 김정윤(Jung-Yoon Kim)

This study analyzes the current status and implications of digital student safety experience centers in facility safety across Korea, the United States, Japan, and Singapore. Korea currently operates 94 student safety experience centers under the Ministry of Education; however, digital technology adoption remains concentrated in a limited number of facilities, and educational content specialized in structural safety?such as aging infrastructure and building collapse?is notably lacking. Through comparative analysis of technological adoption levels, institutional frameworks, content quality management systems, and user safety standards, this study identifies significant gaps in Korea’s digital safety education infrastructure. The United States has established a systematic framework through the NFPA 1035 standard for safety educator qualifications, FEMA’s VR-based fire evacuation training program developed in collaboration with Meta, and the DHS SAVER program that evaluates 21 VR and 11 AR training products for first responders. Japan mandates school safety planning under the School Health and Safety Act and operates well-established disaster prevention facilities such as the Ikebukuro Life Safety Learning Center, which features earthquake simulators and VR disaster simulations. Singapore leverages the SCDF’s integrated governance under the Fire Safety Act and has been actively pursuing digital transformation in civil defence education. Based on these findings, this paper proposes a phased digital transformation roadmap (2025?2033), development of structural safety-specialized content, a quality certification framework for safety experience centers, and legislative and institutional improvements. The research contributes to establishing a comprehensive strategy for digital safety education that addresses both technological and regulatory dimensions in the field of structural maintenance and inspection.