A seismic intensity map, which describes ground motion distribution due to an earthquake, is crucial for disaster evaluation after the event. The ShakeMap system, developed and disseminated by the USGS, is widely used to generate intensity maps in many countries. The system utilizes a semi-variogram model to interpolate the measured intensities at seismic stations spatially. However, the default semi-variogram model embedded in ShakeMap is based on data from high seismic regions, which may not be suitable for the Korean Peninsula, categorized as a low-to-moderate seismic region. To address this discrepancy, this study aims to develop the region-specific semi-variogram model using local records and a region-specific ground motion model (GMM). To achieve this, we followed these steps: 1) collected records from significant earthquake events in South Korea, 2) calculated residuals between the observed intensities and predictions by the GMM, and 3) created semi-variogram models using weighted least squares regression to better fit short separation distances for PGA, PGV, SA0.2, and SA1.0. We compared the developed semi-variogram models with conventional models embedded in ShakeMap. Validation tests showed that the region-specific semi-variogram model reduced the mean squared error of intensity predictions by approximately 3.5% compared to the conventional model.

Reinforced concrete (RC) columns exhibit cyclic damage, such as strength degradation, under cyclic lateral loading, such as earthquakes. Considering the cyclic damage, the nonlinear load-deformation response of RC columns can be simulated using a lumped plasticity model. Based on an experimental database, this study calibrates lumped plasticity model parameters for 371 rectangular and 290 circular RC columns. The model parameters for adequate flexural rigidity, plastic rotation capacity, post-capping rotation capacity, moment strength, and cyclic strength degradation parameter are adjusted to match each experimentally observed load-deformation response. We have developed predictive equations that accurately relate the model parameters to the design characteristics of RC columns through regression analyses, providing a reliable tool for engineers and researchers. To demonstrate their application, the proposed and existing models numerically simulate the earthquake response of a bridge pier in a metropolitan railway bridge. The pier is subjected to several ground motions, increasing intensity until collapse occurs. The proposed lumped plasticity model showed about 41% less vulnerable to collapse.

Truss structures, widely used in engineering, consist of straight members transferring axial forces. Traditional analysis methods like FEM and the Force Method become computationally expensive for large-scale and nonlinear problems. Surrogate models using Artificial Neural Networks (ANNs), particularly Physics-Informed Neural Networks (PINNs), offer alternatives but require extensive training data and computational resources. Variational Quantum Algorithms (VQAs) address these challenges by leveraging quantum circuits for optimization with fewer parameters. Variational Quantum Circuits (VQCs) based on Quantum Neural Networks (QNNs) utilize quantum entanglement and superposition to approximate high-dimensional data efficiently, making them suitable for computationally intensive tasks like surrogate modeling in structural analysis. This study applies QNNs to truss analysis using 6-bar and 10-bar planar trusses, assessing their feasibility. Results indicate that residual-based loss functions enable QNNs to make reliable predictions, with increased layers improving accuracy and a higher Q-bit count contributing to performance, albeit marginally.

Automated structural design methods for reinforced concrete (RC) beam members have been widely studied with various techniques to date. Recently, artificial intelligence has been actively applied to various engineering fields. In this study, machine learning (ML) is adopted to make automated structural design model for RC beam members. Among various machine learning methods, a supervised learning was selected. When a supervised learning is applied to development of ML-based prediction model, datasets for training and test are required. Therefore, the datasets for rectangular and t-shaped RC beams was constructed by commercial structural design software of MIDAS. Five supervised learning algorithms, such as Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost) were used to develop the automated structural design model. Design moment (Mu), design shear force (Vu), beam length, uniform load (wu) were used for inputs of structural design model. Width and height of the designed section, diameter of top and bottom bars, number of top and bottom bars, diameter of stirrup bar were selected for outputs of structural design model. Performance evaluation of the developed structural design models was conducted using metrics sush as root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2). This study presented that random forest provides the best structural design results for both rectangular and t-shaped RC beams.

This study developed an integrated performance evaluation framework for rural development Official Development Assistance (ODA) projects in Korea and validated its effectiveness through practical application. Based on the FAO’s SAFA (Sustainability Assessment of Food and Agriculture Systems) Tool, the framework enables a balanced assessment across economic, social, environmental, and governance dimensions. The methodology incorporates the Analytic Hierarchy Process (AHP) and Importance-Performance Analysis (IPA) to measure the importance and performance of objectives and indicators. The developed framework serves as a tool for Project Design Matrix (PDM) development, monitoring, and evaluation throughout the project’s planning, implementation, and completion phases. During planning, it systematically incorporates stakeholder input in setting objectives and indicators. During implementation, it facilitates real-time monitoring for immediate decision-making and resource reallocation. At completion, it supports comprehensive performance evaluation. Application of the framework to the “Rural Development Programme in Tuyen Quang Province, Vietnam” demonstrated its effectiveness in systematizing excessive indicators and clarifying the hierarchical and logical connections between objectives. This performance evaluation framework can enhance project transparency and accountability by overcoming the limitations of current PDM approaches and providing systematic methods for incorporating stakeholder feedback. It is particularly applicable to multi-sectoral rural development programs and is expected to contribute to integrated development in target areas. However, validation through a single case study presents limitations, necessitating future application across diverse regions and project types to verify generalizability.

본 연구의 목적은 통합사회에서 요구되는 역량인 장애감수성을 함양할 수 있는 시민교육으로서의 건강권 교육을 개발하는 것을 목적으로 한다. 연구방법은 이제까지의 장애인식개선을 위한 교육이 비장애인을 대상으 로 한 장애체험 위주의 교육이었다는 문제의식을 기반으로 실행연구를 선택함으로써 건강권 교육 프로그램을 실행하고 평가하였다. 연구결과는 장애의 재해석을 기반으로 한 본 건강권 교육 프로그램은 장애에 대한 단순한 정보 전달이 아닌, 체험과 성찰을 통해 장애를 새롭게 이해하고 장애인에게 공감하는 역량을 형성하는 데 성공적이었다. 융복합 접근의 교육적 결과는 다학제로 구성된 교육과정과 팀티칭 수업이 장애인의 건 강을 사회모델과 의료모델이라는 이분법이 아닌) 다각도로 이해할 수 있 다는 점에서 긍정적이었으나, 교수자의 지속적 상호작용을 통해 연계에 좀 더 노력을 기울일 필요가 있었다. 장애인의 건강하고 풍요로운 삶을 위한 대안을 모색하기 위해서는 다학제간 융복합적 지원이 필요하다. 또 한 장애인의 요구를 수렴하고 전문적인 해결책을 함께 고민할 수 있는 장애인과 비장애인 간 상호작용이 지속적으로 필요하다는 점을 확인할 수 있었다.

This study is a preliminary investigation into a method for updating analytical models using actual vibration measurement data to improve the reliability of the seismic performance evaluations. The research was conducted on 26 models with various parameters, aiming to develop an optimal analytical model that closely matches the natural frequencies of the actual building. By identifying the dynamic characteristics of the target building through vibration measurements taken just before the demolition of the structure, the natural frequency analysis results of the analytical models were compared to the measured data. Based on this comparison, an optimized method for adjusting the parameters of the analytical models was derived. Throughout the analysis, various parameters were adjusted, and the eigenvalue analysis results were corrected by comparing them with vibration measurements. Among the comparative analytical models, the model with the lowest error rate was selected. The results showed that, in all cases, the analytical model with a concrete compressive strength of 16 MPa (based on actual measurements), pin boundary conditions, and an idealized strip footing cross-section had the closest match to the actual building's natural frequencies, with an average error of less than 8%.

Defense Modeling & Simulation (M&S) techniques are used for developing the efficiency and economics of national defense at operational level so that it maintains interoperability and reusability in sustainability for the following process of the war simulation. However, the lack of conceptual models was one cause of limiting the interoperability and reusability in defense M&S areas. In this paper, the Conceptual Model of the Mission Space (CMMS) is studied as preliminary process for the defense M&S. The conceptual modeling framework called CMMS-K (Conceptual Model of the Mission Space-Korea) is suggested using a case example in consideration of the Korean Army specification and characteristics. The practicality of CMMS-K is evaluated through the ontology development for military scenarios. It is expected that the gap between the theoretical approach and the practical perspective of defense M&S can be diminished through the use of these approaches.

This study aims to improve the interpretability and transparency of forecasting results by applying an explainable AI technique to corporate default prediction models. In particular, the research addresses the challenges of data imbalance and the economic cost asymmetry of forecast errors. To tackle these issues, predictive performance was analyzed using the SMOTE-ENN imbalance sampling technique and a cost-sensitive learning approach. The main findings of the study are as follows. First, the four machine learning models used in this study (Logistic Regression, Random Forest, XGBoost, and CatBoost) produced significantly different evaluation results depending on the degree of asymmetry in forecast error costs between imbalance classes and the performance metrics applied. Second, XGBoost and CatBoost showed good predictive performance when considering variations in prediction cost asymmetry and diverse evaluation metrics. In particular, XGBoost showed the smallest gap between the actual default rate and the default judgment rate, highlighting its robustness in handling class imbalance and prediction cost asymmetry. Third, SHAP analysis revealed that total assets, net income to total assets, operating income to total assets, financial liability to total assets, and the retained earnings ratio were the most influential factors in predicting defaults. The significance of this study lies in its comprehensive evaluation of predictive performance of various ML models under class imbalance and cost asymmetry in forecast errors. Additionally, it demonstrates how explainable AI techniques can enhance the transparency and reliability of corporate default prediction models.