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 with...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.展开更多
This paper describes the effects of high temperatures on the strength characteristic of crushed limestone sand concrete (CLSC). To compare, natural (river) sand concrete (NSC) and CLSC specimens were exposed to the th...This paper describes the effects of high temperatures on the strength characteristic of crushed limestone sand concrete (CLSC). To compare, natural (river) sand concrete (NSC) and CLSC specimens were exposed to the three different high temperatures. Visual color-change and weight loss were also carefully examined through the tests. The test results indicated that the decreasing rate of compressive strength of CLSC after exposure to high temperature is slightly lower than that of NSC while the splitting tensile strength of CLSC indicated a very similar rate compared to NSC. Therefore, the strength variations of crushed limestone sand concrete after exposal to high temperature can be similarly treated as that of the natural sand concrete. Also it can be seen that the CLSC can use 0.5 power law equation to represent the relationship between compressive and splitting tensile strength before and after exposal to high temperature.展开更多
文摘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.
文摘This paper describes the effects of high temperatures on the strength characteristic of crushed limestone sand concrete (CLSC). To compare, natural (river) sand concrete (NSC) and CLSC specimens were exposed to the three different high temperatures. Visual color-change and weight loss were also carefully examined through the tests. The test results indicated that the decreasing rate of compressive strength of CLSC after exposure to high temperature is slightly lower than that of NSC while the splitting tensile strength of CLSC indicated a very similar rate compared to NSC. Therefore, the strength variations of crushed limestone sand concrete after exposal to high temperature can be similarly treated as that of the natural sand concrete. Also it can be seen that the CLSC can use 0.5 power law equation to represent the relationship between compressive and splitting tensile strength before and after exposal to high temperature.
