Nonlinear Pushover Analysis of the Influences on RC Footing for the External Elevator Well

In contemporary society, reducing carbon dioxide emissions and achieving sustainable development are paramount goals. One effective approach is to preserve existing RC (Reinforced Concrete) buildings rather than demolishing them for new construction. However, a significant challenge arises from the lack of elevator designs in many of these existing RC buildings. Adding an external elevator becomes crucial to solving accessibility issues, enhancing property value, and satisfying modern residential buildings using convenient requirements. However, the structural performance of external elevator wells remains understudied. This research is designed by the actual external elevator project into existing RC buildings in Jinzhong Rd, Shanghai City. Specifically, this research examines five different external elevator wells under nonlinear pushover analysis, each varying in the height of the RC (Reinforced Concrete) footing. By analyzing plastic hinge states, performance points, capacity curves, spectrum curves, layer displacement, and drift ratio, this research aims to provide a comprehensive understanding of how these structures of the external elevator well respond to seismic events. The findings are expected to serve as a valuable reference for future external elevator projects, ensuring the external elevator designs meet the seismic requirements. By emphasizing seismic resistance in the design phase, the research aims to enhance the overall safety and longevity of external elevator systems integrated into existing RC buildings.

Nonlinear Time History Analysis for the Different Column Orientations under Seismic Wave Synthetic Approach

The significant impact of earthquakes on human lives and the built environment underscores the extensive human and economic losses caused by structural collapses. Over the years, researchers have focused on improving seismic design to mitigate earthquake-induced damages and enhance structural performance. In this study, a specific reinforced concrete (RC) frame structure at Kyungpook National University, designed for educational purposes, is analyzed as a representative case. Utilizing SAP 2000, the research conducts a nonlinear time history analysis to assess the structural performance under seismic conditions. The primary objective is to evaluate the influence of different column section designs, while maintaining identical column section areas, on structural behavior. The study employs two distinct seismic waves from Abeno (ABN) and Takatori (TKT) for the analysis, comparing the structural performance under varying seismic conditions. Key aspects examined include displacement, base shear force, base moment, joint radians, and layer displacement angle. This research is anticipated to serve as a valuable reference for seismic restraint reinforcement work on RC buildings, enriching the methods used for evaluating structures through nonlinear time history analysis based on the synthetic seismic wave approach.

Prediction and Analysis of Elevator Traffic Flow under the LSTM Neural Network

Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.