This study establishes a structured development procedure for a non-ergodic ground motion model (GMM) and applies it to Korean seismic records to evaluate uncertainty reduction. The proposed framework includes data screening based on signal-to-noise ratio, residual computation relative to NGA-East predictions, identification of systematic trends, and stepwise correction of site, magnitude, and distance effects. A total of 368 records from 16 earthquakes (Mw ≥ 4.0) observed at 53 stations were analyzed. The residuals exhibited clear VS30-dependent trends, particularly at short periods (–0.2 s). Period-dependent VS₃₀ correction terms were derived through linear regression, with additional corrections for magnitude and distance applied when sufficient data were available. Spectral comparisons for the 2016 Gyeongju and 2017 Pohang earthquakes demonstrated improved agreement after calibration. The stepwise corrections resulted in a consistent reduction of total standard deviation across periods, with the largest decrease observed near 0.1 s. These results indicate that the proposed development procedure provides a practical pathway for transitioning from ergodic to partially non-ergodic modeling and effectively reduces aleatory uncertainty for Korean seismic hazard applications.

This study develops a correction model to improve the accuracy of horizontal spectral accelerations estimated by stochastic extended finite-fault simulation (EXSIM) in southeastern Korea. EXSIM predictions for five earthquakes (M4.3-5.5) recorded at eight stations reveal frequency-dependent residuals, with a tendency to underpredict spectral accelerations at frequencies ≥ 3 Hz. These discrepancies are correlated with eight variables: moment magnitude, stress drop, hypocentral distance, azimuth, average shear wave velocity up to 30 m in depth, relative elevation, and slope. To address these discrepancies, a multiple linear regression model is developed using eight variables that reflect earthquake source characteristics, wave-propagation paths, and site-specific conditions, including azimuth and topographic effects not fully accounted for in the original EXSIM. Application of this correction model substantially improves predictive performance, reducing root-mean-square error by 18.8% to 81.0% for the test sets. The corrected response spectra show good overall agreement with observations, including high-frequency spectral peaks. This approach enables the construction of reliable ground-motion databases. It enhances the accuracy of EXSIM predictions for scenario earthquakes, providing a practical tool for seismic hazard assessment in regions with sparse observational data.

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.

최근 수질 오염 문제의 심화와 수자원 회수의 중요성이 부각되면서, 분리막 기반 수처리 기술이 큰 관심을 받고 있다. 분리막 공정은 높은 분리 성능과 낮은 에너지 소비로 인해 기존 전통적 기술보다 친환경적인 대안으로 여겨져 왔으나, 석유 화학 기반 고분자, 단량체 및 유기용매에 의존하는 제조 및 폐기 과정에서의 환경적 한계가 지적되고 있다. 이에 따라 친환경적이고 생분해성이 우수한 천연물질 기반 분리막이 지속 가능한 대안으로 주목받고 있으며, 최근 다양한 연구가 활발 히 진행되고 있다. 본 총설에서는 천연소재를 활용한 수처리 분리막 연구를 소재별로 체계적으로 정리하고, 각 소재의 특성과 활용 사례를 고찰한다. 또한, 이러한 분리막의 잠재적 장점과 한계점을 논의하고, 향후 연구 방향을 제시함으로써 지속 가능 한 수처리 기술 개발을 위한 방향성을 제공하고자 한다.

High-entropy alloys (HEAs) are alloys that contain multiple principal elements, each in the range of 5–35%. HEAs exhibit excellent properties, however, even with conventional trial-and-error, high-throughput experimentation, and computational materials approaches, exploring their vast compositional space remains highly challenging. Accordingly, data-driven machine learning and generative-model-based inverse design methods are increasingly essential. In this study, we propose a generative-model-enabled HEA inverse design framework aimed at improving ultimate tensile strength (UTS). We first compiled 501 HEA data points from published literature and performed statistical analyses to understand their characteristics. Next, we tuned the hyperparameters of XGBoost and random forest (RF) models via Bayesian optimization, compared their performance with that of a deep neural network (DNN), and selected XGBoost as the optimal predictive model. In the subsequent stage, we trained a PyTorch-based variational autoencoder (VAE) on data from regions of the latent space associated with high-UTS probability. We randomly sampled 1,000 latent vectors, decoded them to generate candidate alloy compositions, and evaluated these candidates using the optimized XGB model. Finally, Shapley additive explanations (SHAP) interpretability analysis and a network plot were used to quantify the contributions and interactions of each feature variable, thereby assessing the physical plausibility of the model-suggested compositions.

Multivalent ions in natural aqueous solutions—such as seawater, brackish water, and freshwater—can negatively affect the performance of ion exchange membranes (IEMs) used in electrochemical energy and environmental devices. In this study, a pore-filling cation exchange membrane (CEM) permeable to multivalent ions was fabricated to minimize performance degradation caused by such ions. To achieve this, multilayer pore-filling CEMs were prepared by performing two impregnation processes using monomer electrolyte solutions of different compositions (varying deionized water content and monomerto- crosslinker ratios). As a result, a highly crosslinked electrolyte polymer formed on the internal side of the CEM, while a low-crosslinked polymer formed on the external side. Due to the presence of the low-crosslinked outer polymer layer, the multilayer pore-filling CEM exhibited a smaller increase in resistance caused by Mg2+ ions. Furthermore, based on the correlation between permselectivity and resistance measured in a 0.45 M NaCl + 0.05 M MgCl2 solution, which simulated the Mg2+ concentration in seawater, an optimal structure of multilayer pore-filling CEM was identified, and it exhibited a minimized increase in resistance and a permselectivity of over 90 %.

This study investigates the risk reduction effect and identifies the optimal capacity of Multi-barrier Accident Coping Strategy (MACST) facilities for nuclear power plants (NPPs) under seismic hazard. The efficacy of MACST facilities in OPR1000 and APR1400 NPP systems is evaluated by utilizing the Improved Direct Quantification of Fault Tree with Monte Carlo Simulation (I-DQFM) method. The analysis encompasses a parametric study of the seismic capacity of two MACST facilities: the 1.0 MW large-capacity mobile generator and the mobile low-pressure pump. The results demonstrate that the optimal seismic capacity of MACST facilities for both NPP systems is 1.5g, which markedly reduces the probability of core damage. In particular, the core damage risk is reduced by approximately 23% for the OPR1000 system, with the core damage fragility reduced by approximately 72% at 1.0g seismic intensity. For the APR1400 system, the implementation of MACST is observed to reduce the core damage risk by approximately 17% and the core damage fragility by approximately 44% under the same conditions. These results emphasize the significance of integrating MACST facilities to enhance the resilience and safety of NPPs against seismic hazard scenarios, highlighting the necessity for continuous adaptation of safety strategies to address evolving natural threats.

본 논문에서는 지진 하중으로 인한 급격한 구조손상탐지를 수행하기 위해 분산점 칼만필터(Unscented Kalman Filter, UKF)와 파티 클 필터(Particle Filter)를 소개하고 지진 손상 시나리오에 적용 및 비교･검토하였다. 이때, 비선형 전단 빌딩을 모사하기 위해 Bouc-Wen 모델을 사용하였고, 급격한 변화를 추정하기 위해 추가적으로 적응형 기법(Adaptive rule)인 Adaptive Jumping Method를 두 필터 모두에 적용하였다. 적용 결과 두 오리지날 필터 모두 급격한 손상 시점과 정도를 파악하지 못하였고, 적응형 기법을 반영하였 을 경우에만 시점 파악이 가능하였다. 하지만, 여전히 손상 정도를 정확히 파악하지 못하였고, 두 방법 모두 제안된 적응형 기법을 새 로이 조정하였을 경우에 정확한 추정이 가능함을 확인하였다. 최종적으로 계산시간을 고려하였을 때, 새로운 형태의 적응형 기법을 적용한 UKF 사용을 제안하는 것으로 비교 검토를 수행하였다.

본 연구에서는 공기역학적 형상변화의 풍하중 저감 측면에서의 효율성을 평가하기 위해 평면의 모서리 부분이 개선된 고층 건물에 대해 사례연구 기반의 비탄성 내풍설계를 수행하였다. 비선형 시간이력해석을 통해 다양한 설계풍속 및 항복 후 강성에 대한 구조물의 응답을 산정하였으며, 최근 국내 설계기준(KDS 41)에 도입된 성능기반내풍설계 개념을 토대로 구조물의 성능을 평가하였다. 해석 결과 공기역학적 형상변화를 갖는 구조물의 경우나 성능기반내풍설계를 적용했을 경우(또는 모두에 해당할 경우) 공진성분을 줄 여 구조물의 응답이 크게 감소함을 확인하였다.