본 연구는 설계변경이 빈번하게 발생하는 민간공사의 단가도급 건축 인테리어 공사에서 각 공사의 기성율에 약정금액을 적용하여 공사대금을 산정하는 공사 대금 감정시, 설계 변경 전의 ‘미시공(제외) 물량’과 기시공 부분의 물량 증감에 대한 공사 내용을 내역서와 공정표를 기반으로 하여 약정금액을 감정인이 임 의로 정하게 되는 경우를 단계별 COST & TIME TABLE분석을 통한 변경약정 금액 적용에 대해 연구한 것이다. 계약 시 또는 공사중 변경 계약된 약정 총공사비를 기준으로 각 공정의 계획물 량과 실적물량을 종합하여 기성율을 산정하고 약정금액을 곱해 기성고 및 추 가공사대금을 산정한다. 기성고 공사대금 산정 시 약정을 우선하고 추가공사대금 약정이 되어 있지 않 더라도 감정 신청에 따라 추가 공사비 약정 존부를 법원에서 최종 결정하기 전 감정인이 추가 공사한 사실을 전제로 감정인이 감정서에 추가공사비의 기성율과 추가공사대금을 산출하게 되는데 각 공사대금의 약정금액은 기시공에 소요된 공사비 이외 설계변경 발생시 미시공(제외)물량과 물량증감, 기시공 중 철거후재시공, 변경시공, 기시공의 하자보수와 지연분석 및 공정표를 함께 고 려해서 최종 약정금액을 적용해야 한다.

Reinforcement learning (RL) is successfully applied to various engineering fields. RL is generally used for structural control cases to develop the control algorithms. On the other hand, a machine learning (ML) is adopted in various research to make automated structural design model for reinforced concrete (RC) beam members. In this case, ML models are developed to produce results that are as similar to those of training data as possible. The ML model developed in this way is difficult to produce better results than the training data. However, in reinforcement learning, an agent learns to make decisions by interacting with an environment. Therefore, the RL agent can find better design solution than the training data. In the structural design process (environment), the action of RL agent represent design variables of RC beam. Because the number of design variables of RC beam section is many, multi-agent DQN (Deep Q-Network) was used in this study to effectively find the optimal design solution. Among various versions of DQN, Double Q-Learning (DDQN) that not only improves accuracy in estimating the action-values but also improves the policy learned was used in this study. American Concrete Institute (318) was selected as the design codes for optimal structural design of RC beam and it was used to train the RL model without any hand-labeled dataset. Six agents of DDQN provides actions for beam with, beam depth, bottom rebar size, number of bottom rebar, top rebar size, and shear stirrup size, respectively. Six agents of DDQN were trained for 5,000 episodes and the performance of the multi-agent of DDQN was evaluated with 100 test design cases that is not used for training. Based on this study, it can be seen that the multi-agent RL algorithm can provide successfully structural design results of doubly reinforced beam.

Tuned Mass Dampers (TMDs) are widely used to mitigate structural vibrations in buildings and bridges. However, conventional optimization methods often struggle to achieve optimal performance due to the complexity of structural dynamics. This study proposes the NN-L-BFGS-B algorithm, which combines Artificial Neural Networks (ANNs) for global exploration and L-BFGS-B for local exploitation to efficiently optimize TMD parameters. A ten-story shear-building model with a TMD is used for validation. The proposed method achieves the lowest H₂ norm compared to previous studies, demonstrating improved optimization performance. Additionally, NN-L-BFGS-B effectively balances computational efficiency and accuracy, making it adaptable to various engineering optimization problems.

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.