This study investigates the seismic behavior of low-aspect-ratio reinforced concrete (RC) shear walls when subjected to bi-axial lateral loading, using nonlinear finite element analysis. A three-dimensional finite element model was developed with the DIANA program and validated against previously reported experimental results. Subsequently, a parametric study was conducted by varying the wall aspect ratio of horizontal reinforcing bars under both uni-axial and bi-axial loading conditions. The analysis results show that bi-axial loading reduces shear strength by a significant amount compared to uni-axial loading, and the reduction becomes more pronounced as the aspect ratio decreases. For low-aspect-ratio walls, the influence of horizontal reinforcement on shear strength was limited, while sensitivity to bi-axial loading increased. These findings indicate that uni-axial loading–based evaluation methods may overestimate the seismic capacity of low-aspect-ratio RC shear walls.

This study investigated the structural performance of Cast-in-Place (CIP) pile-integrated composite basement walls (CIP-CBW) featuring socket-type shear connectors (SSCs) in both steel and RC applications through finite element analysis. The results demonstrated that while the height and spacing of SSCs significantly influenced the ultimate load and corresponding displacement of the CIP-CBW, their impact on initial stiffness was negligible. Due to the leverage effect, shear forces along the interface between the CIP pile and the basement wall were resisted by both the front and rear bolts of the SSC. The failure mechanism of the SSC joint was characterized by concrete crushing and cracking around the connector, followed by the formation of plastic hinges in both bolts. Bending moment analysis revealed that the rear bolt is particularly susceptible to flexural yielding. Furthermore, the slip tendency at the interface was more pronounced in the steel scheme than in the RC scheme. Notably, the effect of SSC spacing on slip was significant, whereas SSC height exhibited minimal influence.

This study aims to refine the existing shear strength model for reinforced concrete(RC) beam–column connections by explicitly incorporating the bi-directional loading effect, which more accurately reflects the actual loading conditions of RC structures during earthquakes. A new database consisting of 21 RC beam–column connection specimens tested under simultaneous bi-directional loading was collected and analyzed to investigate the influence of key parameters on joint shear strength. The results revealed that the joint configuration and the presence of a slab are the primary factors governing the extent of bi-directional loading effect on joint shear strength. Based on these findings, a set of simple and practical modification factors was proposed to refine the existing joint shear strength model to account for bi-directional loading effect. The outcomes of this study provide a rational basis for incorporating bi-directional loading effect into the shear strength evaluation of RC beam–column connections

The integrity of interlayer bonding in asphalt pavements is a critical factor to ensure the structure behaves as a unified, monolithic system. Common issues like dust contamination on the receiving surface and inadequate tack coat application create weak interfacial planes that promote localized shear deformation specifically in high-traction zones like braking and turning areas. This study introduces a transferable framework that integrates lab-based interlayer bond characterization, composite fatigue testing, and finite element (FE) modeling to assess pavement performance under realistic field conditions.Two tack coats were used in this study, including regular tack coat (RSC-4) and clean tack coat (ILT-4) and considered 0%, 50% (remaining 50% was covered with dust), and 100% of the contact surface area, at three distinct tack coat application rates. Peak shear strength, initial stiffness, and fractured energy were determined from monotonic shear tests for quantifying bonding state and for FE simulations. Four-point bending (4PB) test was used to characterize fatigue performance, using normalized stiffness s(N), fatigue life and mid-life degradation rate or damage rate (DR). To relate the findings with field behavior, FE simulations estimate shear demand during braking, allowing a demand-to-capacity comparison. Results indicate that dust samples have 10%-30% lower bonding strength and must reach shear fail at the service life at the breaking zone with -0.93 midlife damage rate. Considering DR as a primary performance indicator, the framework provides the ultimate recommendations such as ensure surface cleanliness, uniform tack coat application, and quality control in high-stress zones.

전단 보강근이 불충분한 철근콘크리트(RC) 기둥에서 발생하는 전단 파괴 및 이에 따른 축 붕괴는 매우 치명적인 파괴 유형이다. 기 존의 모델들은 힘 모멘트-전단력-축력 간의 복잡한 상호작용을 모사하는 데 한계가 있는 반면, 정밀 유한요소해석법은 전체 골조 해 석에 적용하기에는 연산 비용이 높다는 단점이 있다. 이에 본 연구에서는 재료 수준의 정밀도와 선요소의 해석 효율성을 결합한 새로 운 매크로모델을 제안한다. 제안된 모델은 기둥을 3개의 요소로 분할한다. 면요소에는 수정 압축장 이론(MCFT)을 도입한 4절점 평 면 응력 정식화를 적용하여, 콘크리트의 2축 응력 상태와 압축 연화(Compression Softening) 효과를 고려하였다. 또한, 해석의 수렴성 과 평형 조건을 만족시키기 위해 이중 중첩 반복 계산 알고리즘을 개발하였다. 실험 데이터와의 검증 결과, 제안 모델은 기존 파이버 모델의 한계를 극복하고 최대 강도 이후의 내력 저하 및 전단 파괴 거동을 성공적으로 예측함을 확인하였다.

This study compares the shear behavior of anisotropic magnetorheological elastomers (MREs) using natural rubber (NR) and silicone rubber (Si) as matrices. The effects of magnetic flux density and compressive pre-stress on the shear modulus were experimentally investigated. Results showed that silicone-based MREs exhibited a 10–20% higher magnetorheological effect than NR-based ones due to stronger particle–matrix bonding and stable chain alignment under magnetic fields. In contrast, NR-based MREs showed greater stiffness variation under compressive stress, attributed to strain-hardening and volumetric constraint effects. These findings indicate that matrix selection significantly governs the magneto-mechanical response: silicone MREs are suitable for precision control and sensing, while NR MREs perform better in high-stress damping systems. This study provides fundamental insight for tailoring MREs according to design requirements.

This study compares the shear behavior of anisotropic magnetorheological elastomers (MREs) using natural rubber (NR) and silicone rubber (Si) as matrices. The effects of magnetic flux density and compressive pre-stress on the shear modulus were experimentally investigated. Results showed that silicone-based MREs exhibited a 10–20% higher magnetorheological effect than NR-based ones due to stronger particle–matrix bonding and stable chain alignment under magnetic fields. In contrast, NR-based MREs showed greater stiffness variation under compressive stress, attributed to strain-hardening and volumetric constraint effects. These findings indicate that matrix selection significantly governs the magneto-mechanical response: silicone MREs are suitable for precision control and sensing, while NR MREs perform better in high-stress damping systems. This study provides fundamental insight for tailoring MREs according to design requirements.

Reinforced concrete structures require effective strengthening methods to improve shear capacity and ductility. Conventional external systems such as steel plates or CFRP sheets are limited by premature debonding and member damage. This study experimentally evaluated the shear performance of concrete beams strengthened with iron-based shape memory alloy (Fe-SMA) strips. Static loading tests compared the effects of prestressing activation, retrofit type and retrofit ratio. The activation of Fe-SMA effectively delayed the formation of shear cracks and reduced width. Also, the Fe-SMA suppressed the shear deformation of stirrups and concrete, resulting in enhanced shear performance and ductility of the strengthened beams. Overall, the Fe-SMA strengthening method was found to be effective in improving the serviceability and maintenance performance of reinforced concrete beams.

본 연구에서는 피난안전구역이 한 개 층 설치된 준초고층 골조-전단벽 건물의 풍하중에 대한 거동을 파악하기 위하여 KDS2022의 스펙트럼을 이용하여 풍방향 및 풍직각방향 풍하중을 생성하고, 생성한 풍하중을 적용하여 임의의 층에 벨트 트러스가 설치된 골조- 전단벽 건물의 동적 해석을 실시하여 풍하중에 대한 동적 거동을 분석한다. 코어의 위치에 따른 편심과 벨트 트러스가 동적 응답에 미 치는 영향을 평가하고자 한다.

This study explores the seismic performance of steel diaphragm walls in underground structures, a critical aspect of structural engineering. The study focuses on the effects of slab diaphragm flexibility, an often overlooked factor in seismic design. Traditional seismic designs often assume the slab acts as a rigid diaphragm, leading to inaccuracies in predicting how forces are distributed between the slab and walls during an earthquake. To address this, the authors model steel diaphragm walls using equivalent cross-sections and analyze shear forces in both rigid and semi-rigid diaphragm scenarios. Results show that semi-rigid diaphragms reduce the shear forces on the exterior walls while increasing them on the internal core, thereby affecting the overall stiffness of the structure. The study emphasizes the importance of considering diaphragm flexibility in seismic design to achieve more accurate predictions of structural behavior and improve construction efficiency.

IIn the context of site response analysis, the use of shear wave velocity (  ) profiles that consider the seismological rock (  ≥ 3,000 m/s) depth is recommended. This study proposes regression analysis and machine learning-based models to predict deep   profiles for a specialized excavated rock site in South Korea. The regression model was developed by modifying mathematical expressions from a previous study and analyzing the correlation between   and model variables to predict deep   beyond 50 m. The machine learning models, designed using tree-based algorithms and a fully connected hierarchical structure, were developed to predict   from 51 m to 300 m at 1 m intervals. These models were validated by comparing them with measured deep   profiles and accurately estimating the trend of deep   variations. The proposed prediction models are expected to improve the accuracy of ground motion predictions for a specialized excavated rock site in Korea.

In this study, the pre-stress characteristics of magnetic rheological rubber, an intelligent material widely applied to mechanical systems, are measured. Intelligent materials are substances that change their properties in response to external inputs and are extensively used in mechanical systems. Magnetic rheological rubber is a representative intelligent material that can exhibit variable characteristics depending on the conditions. When measuring the physical properties of magnetic rheological rubber, it is placed in a magnetic field application device, where a magnetic field is applied, and the material is subjected to pre-stress. Similarly, when manufacturing intelligent mechanical systems using magnetic rheological rubber, pre-stress is induced by components used to apply the magnetic field. Generally, when a material is subjected to pre-stress, its properties change. Consequently, the performance of magnetic rheological rubber under pre-stress also varies. If the characteristics of the material under pre-stress change, the expected performance during design may deviate, leading to differences in the mechanical system's performance from the intended design. This variability makes it challenging to design mechanical systems based on intelligent materials, highlighting the importance of experimentally investigating their characteristics. Therefore, this study measures and identifies the pre-stress characteristics of magnetic rheological rubber under pre-stress. These findings can be applied to improve the measurement methods and design approaches for magnetic rheological rubber in pre-stressed conditions.

압축하중을 받는 콘크리트 충전 강관(CFT) 부재는 강관에 의한 심부구속효과로 인해 내부 콘크리트의 취성이 감소하며, 이는 CFT 부재의 압축강도를 크게 증가시킨다. 본 연구에서는 강관을 퍼포본드 리브 전단연결재로 보강하여 콘크리트의 심부구속효과를 향상 시키고, 유한요소해석 모델에서 재료 특성 및 경계 조건을 설정하여 이를 평가하였다. 이때, 강재와 콘크리트 사이의 계면 거동을 보 다 정확하게 모사하기 위해 cohesive element를 사용하였으며, 이를 통해 강관과 콘크리트 간 하중 전달을 모델링하였다. 전단연결재 로 인한 심부구속효과 향상을 검증하기 위해 퍼포본드 리브 전단연결재가 적용된 CFT 부재의 축소 모델에 대한 실험 및 유한요소해 석을 수행하였고, 전단연결재가 없는 CFT 부재와의 비교를 통해 성능 향상이 확인되었다. 퍼포본드 리브 전단연결재는 홀의 지름 및 개수를 변화시키며 파라미터 스터디를 수행하였고, 이로 인해 전단연결재의 전단저항력 변화가 심부구속효과에 미치는 영향에 대해 평가하였다. 전단연결재의 전단저항력 변화에 따른 CFT 부재의 구조적 성능 차이를 분석하면서, 실험과 유한요소해석의 일치성을 검토하였다.

Liquid hydrogen, a promising energy carrier, necessitates robust storage and transportation systems due to its extremely low boiling point. Consequently, the development of reliable cryogenic adhesives and standardized testing protocols is crucial. This study focused on optimizing the design of a gripper used in single lap shear tests for evaluating cryogenic adhesives, specifically targeting the challenges posed by low-temperature conditions that induce slippage at the gripper interface. The optimal design was performed using a total of five variables, including the position and size of the gripper. By employing the genetic algorithm coupled with finite element analysis, we exhaustively searched through over 1000 models to identify the optimal gripper geometry. We successfully minimized stress concentration at the gripper region while maintaining a uniform stress distribution on the non-bonded surface. Furthermore, the study explored the impact of symmetric versus asymmetric gripper configurations on test results. The findings revealed that symmetric grippers generally yielded more consistent and reliable data. This study's results enable the accurate and stable execution of lap shear tests under the temperature conditions of liquefied hydrogen.

Strong ground motions at specific sites can cause severe damage to structures. Understanding the influence of site characteristics on the dynamic response of structures is crucial for evaluating their seismic performance and mitigating the potential damage caused by site effects. This study investigates the impact of the average shear wave velocity, as a site characteristic, on the seismic response of low-to-medium-rise reinforced concrete buildings. To explore them, one-dimensional soil column models were generated using shear wave velocity profile from California, and nonlinear site response analyses were performed using bedrock motions. Nonlinear dynamic structural analyses were conducted for reinforced concrete moment-resisting frame models based on the regional information. The effect of shear wave velocity on the structural response and surface ground motions was examined. The results showed that strong ground motions tend to exhibit higher damping on softer soils, reducing their intensity, while on stiffer soils, the ground motion intensity tends to amplify. Consequently, the structural response tended to increase on stiffer soils compared to softer soils.

노후 건축물은 불충분한 전단성능으로 인해 위험성이 증가하고 있다. 특히, 콘크리트 보의 전단 성능은 구조물의 붕괴를 지 연시키는 것에 있어 중요하다. 이를 개선하기 위해 본 연구는 철근콘크리트보의 전단보강 기법을 제안하고 성능을 실험적으로 평가하 였다. 이를 위해 기존 니켈-티타늄계 형상기억합금보다 경제성이 우수한 철계 형상기억합금(Fe SMA)을 선정하였다. 불충분한 내부 횡 방향 철근이 반영된 세 개의 콘크리트 보를 제작하였고 무보강, 100mm 간격, 200mm 간격의 보강 간격을 적용하였다. 정적가력시험 결과, 보강된 시험체가 강성 증진에 효과적인 것으로 밝혀졌다. 특히, 200mm 간격의 보강은 콘크리트 보의 연성적인 휨거동도 이끌어 내었다.

Environmental pollution has led to global warming, which threatens human life. In response, hydrogen is gaining attention as a next-generation energy source that does not emit carbon. Due to its explosive nature, special care must be taken in the safe storage and transportation of hydrogen. Among various storage methods, liquefied storage, which can reduce its volume to 1/800, is considered efficient. However, since its boiling point reaches -253°C, the design of an insulation system is essential. For the design of insulation systems applied to large containers, a membrane-type design is required, which necessitates the use of cryogenic adhesives. To evaluate whether the cryogenic adhesive is properly implemented, assessments such as tensile and shear tests are necessary. This study presents a methodology for shear evaluation. Conventional methods for shear evaluation of adhesives result in slippage, preventing proper assessment. Therefore, a method involving drilling holes in the gripper and pulling from the holes must be applied. Optimal design concerning the size and location of the holes is required, and this study derives optimal values based on finite element analysis. By conducting experiments based on the results of this study, it is expected that the risk of gripper damage will be minimized, allowing for accurate evaluation of the adhesive’s performance.

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

본 연구에서는 구조물의 재료, 구조물의 단면, 지진 하중등의 불확실성을 고려한 저형 전단벽의 최대 전단력를 예측하는 뉴 런-네트워크 모델을 개발하였다. 이를 위해 실험 데이터를 통해 검증된 박스타입 저형 전단벽 수치해석 모델을 구축하였고, 가정된 분 포를 통해 200개의 구조물의 재료, 단면변수를 라틴 하이퍼 큐브 샘플링을 통해 추출하였다. 또한 이전 연구에서 사용된 인공지진파를 데이터를 기반으로 10개의 다른 PGA 레벨별 총 200개의 인공지진파 데이터를 구축하였다. 뉴런-네트워크 모델의 Training 및 testing을 위해 200개의 데이터셋에 상응 수치해석 모델을 구축하고 최대 전단력을 산출하였다. 이렇게 구축된 데이터셋을 이용하여 최종적으로 뉴런-네트워크 모델을 확정하였다. 마지막으로 구축된 모델로부터 얻어진 취약도와 기존에 사용되는 방법들로부터 얻은 취약도를 비교, 분석하여 본 연구에서 구축된 모델의 정확도를 보여주었다.