This paper examines recent trends in stadium design and construction through a comprehensive case study and literature review. It highlights the innovative use of advanced building materials, such as high-performance composites and sustainable concrete mixes, which enhance structural integrity while reducing environmental impact. The integration of smart technologies—including IoT technologies, building information modeling (BIM), and digital twin—is explored for its role in improving operational efficiency, safety, and maintenance processes. Additionally, the study reviews the development of cutting-edge engineering techniques like seismic design, advanced AI-based structural analysis, which streamline construction processes and optimize resource usage. Emphasizing sustainability, the paper also discusses strategies for energy-efficient designs and renewable energy integration. Overall, the findings demonstrate how interdisciplinary approaches combining material science, smart technology, and sustainable engineering are shaping the future of stadium construction.

In this paper, machine learning models were applied to predict the seismic response of steel frame structures. Both geometric and material nonlinearities were considered in the structural analysis, and nonlinear inelastic dynamic analysis was performed. The ground acceleration response of the El Centro earthquake was applied to obtain the displacement of the top floor, which was used as the dataset for the machine learning methods. Learning was performed using two methods: Decision Tree and Random Forest, and their efficiency was demonstrated through application to 2-story and 6-story 3-D steel frame structure examples.

In this study, the evaluation items related to the effectiveness evaluation of the LVC (Live, Virtual, Constructive) training system of the Air Force were derived and the weights of each item were analyzed. The LVC training system evaluation items for AHP (Analytic Hierarchy Process) analysis were divided into three layers, and according to the level, 3 items were derived at level 1, 11 items at level 2, and 33 items at level 3. For weight analysis of evaluation items, an AHP-based pairwise comparison questionnaire was conducted for Air Force experts related to the LVC training system. As a result of the survey, related items such as (1) Achievement of education and training goals (53.8%), (1.2) Large-scale mission and operational performance (25.5%), and (1.2.1) Teamwork among training participants (19.4%) was highly rated. Also, it was confirmed that the weights of evaluation items were not different for each expert group, that is, the priority for importance was evaluated in the same order between the policy department and the working department. Through these analysis results, it will be possible to use them as evaluation criteria for new LVC-related projects of the Air Force and selection of introduction systems.

In this paper, a GAN-based data augmentation method is proposed for topology optimization. In machine learning techniques, a total amount of dataset determines the accuracy and robustness of the trained neural network architectures, especially, supervised learning networks. Because the insufficient data tends to lead to overfitting or underfitting of the architectures, a data augmentation method is need to increase the amount of data for reducing overfitting when training a machine learning model. In this study, the Ganerative Adversarial Network (GAN) is used to augment the topology optimization dataset. The produced dataset has been compared with the original dataset.

This paper describes an adaptive hybrid evolutionary firefly algorithm for a topology optimization of truss structures. The truss topology optimization problems begins with a ground structure which is composed of all possible nodes and members. The optimization process aims to find the optimum layout of the truss members. The hybrid metaheuristics are then used to minimize the objective functions subjected to static or dynamic constraints. Several numerical examples are examined for the validity of the present method. The performance results are compared with those of other metaheuristic algorithms.

느타리 1 kg 봉지재배를 통해 살균배지의 주요 화학성과 2주기까지의 버섯 수량간 관계를 분석한 결과, 버섯 수확량은 pH, 총질소 함량, CN율과 2차 함수식에서 매우 높은 상관을 보였다. pH는 4.9~5.0의 범위에서, 총 질소량은 2.0~2.2%에서, CN율은 20~22.5에서 가장 높은 수량성을 보였다. 1주기 버섯은 pH, 총질소량 및 CN율과 높은 상관을 보였고 2주기 버섯은 CN율 외에는 상관이 나타나지 않았다. 그러나 수량합계가 1주기 수량보다 3종의 화학성과의 관계에서 모두 더 높은 상관이 나타났다. 그리하여 본 연구에서 배지의 pH, 총질소 함량 및 CN율은 전 수확기간에 걸쳐서 버섯수량에 큰 영향을 미친다는 결론을 얻을 수 있었다.

This paper describes a novel zero-stress member selecting method for sizing optimization of truss structures. When a sizing optimization method with static constraints is implemented, the member stresses are affected sensitively with changing the variables. However, because some truss members are unaffected by specific loading cases, zero-stress states are experienced by the elements. The zero-stress members could affect the computational cost and time of sizing optimization processes. Feature selection approaches can be then used to eliminate the zero-stress member from the whole variables prior to the process of optimization. Several numerical truss examples are tested using the proposed methods.

This study examined the physicochemical properties of radish pickle containing different natural preservatives (grapefruit seed extract, green tea extract, rosemary, or olive) stored for 0, 1, 2, 3, and 4 weeks. The hardness and color of the radish pickles with the grapefruit seed extract was higher than the other radish pickles during storage from week 0 to week 4. A 14.52% and 13.80% decrease in hardness and color were observed in the radish pickles with grapefruit seed extract (GFE), respectively. In addition, the total phenolic content was highest in the GFE in natural preservatives. Based on the results, GFE was selected as the optimal natural preservatives, and the growth of total viable bacteria and yeast were evaluated. The total viable bacteria and yeast showed similar patterns to the control. These results are expected to be useful in producing radish pickles with optimal quality and contribute to the development of various foods in the food industry.

There has been increasing interest in UHPC (Ultra-High Performance Concrete) materials in recent years. Owing to the superior mechanical properties and durability, the UHPC has been widely used for the design of various types of structures. In this paper, machine learning based compressive strength prediction methods of the UHPC are proposed. Various regression-based machine learning models were built to train dataset. For train and validation, 110 data samples collected from the literatures were used. Because the proportion between the compressive strength and its composition is a highly nonlinear, more advanced regression models are demanded to obtain better results. The complex relationship between mixture proportion and concrete compressive strength can be predicted by using the selected regression method.

산느타리 배지재료별 적정 혼합비율을 구명하기 위하여 1 kg 봉지재배를 통해 배지의 화학성과 2주기까지의 버섯 수량간 관계를 분석한 결과, 1주기 버섯 수확량은 총 질소량과 상관을 보였는데, 총 질소량 1.1% 부근에서 최대 의 수량을 보였고 그보다 질소량이 적거나 많은 경우 수 량이 적어지는 경향을 보였다. 또한 2주기 버섯 수확량은 총질소, 총탄소, C/N율 등 여러 가지 화학성과 높은 상관을 보였는데, 질소함량 1% 이상의 구간에서 질소함량이 높을수록, 탄소함량 13% 이상의 구간에서 탄소함량이 높을수록 수량이 증가되는 양상을 보였다. 따라서 산느타리 재배시 혼합배지의 질소함량이 1.1% 정도인 경우 1주기 버섯수량이 가장 높았고, 2주기 수량은 질소와 탄소의 함량이 높은 배지에서 높은 경향을 보였다. 다만, 면실피가 다량 첨가된 배지에서 대두박과 케이폭박이 합계 20% 첨가되면 버섯 발생에 문제가 생길 수 있으므로 주의해야 할 것이다.