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.

Performance-Based Seismic Design (PBSD) is an approach that evaluates how structures will perform under different levels of seismic activity. It focuses on ensuring that buildings not only withstand earthquakes but also meet specific performance objectives, such as minimizing damage or maintaining functionality after the event. Unlike traditional methods, PBSD allows for more tailored, cost-effective designs by considering varying degrees of acceptable damage based on the structure's importance and use. PBSD was introduced in Korea in 2016 to replace elastic design, which is inevitable to over-design to cope with all variables such as earthquakes and winds. When PBSD is applied to the structural design new building, One of the challenges of PBSD is the complexity involved in creating accurate inelastic analysis models. The process requires significant time and effort to analyze the results, as it involves detailed simulations of how structures will behave under seismic stress. Additionally, organizing and interpreting the analysis data to meet performance objectives can be labor-intensive and technically demanding. In order to solve this problem, a post-processor program was developed in this study. A post-processor was developed based on Excel program using Visual Basic for Applications(VBA). Because analysis outputs of Perform-3D, that is a commercial software for structural analysis and design, are very complicated, generation of tables and graphs for report is significant time and effort consuming task. When the developed post-processor is used to make the seismic design report, the required task time is significantly reduced.

When considering military operations that require rapid response time, forward supply operation of various type of ammunition is essential. Also, t is necessary to supply ammunition in a timely manner before an ammunition shortage situation occurs. In this study, we propose a mathematical model for allocation of ammunition to ammunition storehouse at the Ammunition Supply Post (ASP). The model has several objectives. First, it ensures that the frequent used ammunition is stored in a distributed manner at a high workability ammunition storehouses. Second, infrequent used ammunition is required to be stored intensively at a single storehouse as much as possible. Third, capacity of the storehouse and compatible storage restriction required to be obeyed. Lastly, criticality of ammunition should be considered to ensure safety distance. We propose an algorithm to find the pareto-based optimal solution using the mathematical model in a reasonable computation time. The computational results show that the suggested model and algorithm can solve the real operational scale of the allocation problem.