Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Smart Structures and Systems Volume 33, Number 5, May 2024 , pages 335-347 DOI: https://doi.org/10.12989/sss.2024.33.5.335 |
|
|
|
Deflection aware smart structures by artificial intelligence algorithm |
||
Qingyun Gao, Yun Wang, Zhimin Zhou and Khalid A. Alnowibet
|
||
| Abstract | ||
| There has been an increasing interest in the construction of smart buildings that can actively monitor and react to their surroundings. The capacity of these intelligent structures to precisely predict and respond to deflection is a crucial feature that guarantees both their structural soundness and efficiency. Conventional techniques for determining deflection often depend on intricate mathematical models and computational simulations, which may be time- and resource-consuming. Artificial intelligence (AI) algorithms have become a potent tool for anticipating and controlling deflection in intelligent structures in response to these difficulties. The term "deflection-aware smart structures" in this sense refers to constructions that have AI algorithms installed that continually monitor and analyses deflection data in order to proactively detect any problems and take appropriate action. These structures anticipate deflection across a range of operating circumstances and environmental factors by using cutting-edge AI approaches including deep learning, reinforcement learning, and neural networks. AI systems are able to predict real-time deflection with high accuracy by using data from embedded sensors and actuators. This capability enables the systems to identify intricate patterns and linkages. Intelligent buildings have the potential to self-correct in order to reduce deflection and maximize performance. In conclusion, the development of deflection-aware smart structures is a major stride forward for structural engineering and has enormous potential to enhance the performance, safety, and dependability of designed systems in a variety of industries. | ||
| Key Words | ||
| applied voltage; artificial intelligence algorithm; DQA; HDQM; piezoelectric materials | ||
| Address | ||
| (1) Qingyun Gao, Yun Wang, Zhimin Zhou: FAIR FRIEND Institute of Intelligent Manufacturing, Hangzhou Vocational & Technical College, Hangzhou 310018, China; (2) Khalid A. Alnowibet: Statistics and Operations Research Department, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia. | ||