Research on intelligent semi-active control algorithms and seismic reliability based on machine learning

Aiming to address the shortcomings of existing semi-active control algorithms with poor robustness and the limited generalization ability of current evaluation methods based on deterministic analysis, a novel approach based on probability density evolution is proposed. This method is designed to ass...

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Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 29487 - 27
Main Authors: Xiao, Zhongyuan, Xu, Jianguo, Wang, Li, Huang, Liang
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 27.11.2024
Nature Publishing Group
Nature Portfolio
Subjects:
639/166/986
639/766/259
Algorithms
BP neural network
Control algorithms
Design optimization
Earthquakes
Engineering
Humanities and Social Sciences
Learning algorithms
Machine learning
Modified Optimization Algorithm for Sand Cat Colonies
multidisciplinary
Probability density evolution
Probability learning
Science
Science (multidisciplinary)
Seismic activity
Seismic reliability
Semi-active control
Water diversion
Water supply
BP neural network
Seismic reliability
Modified Optimization Algorithm for Sand Cat Colonies
Semi-active control
Probability density evolution
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Aiming to address the shortcomings of existing semi-active control algorithms with poor robustness and the limited generalization ability of current evaluation methods based on deterministic analysis, a novel approach based on probability density evolution is proposed. This method is designed to assess the seismic reliability, enabling a more comprehensive evaluation of the control effectiveness of aqueduct structures. Building upon this, an intelligent semi-active control algorithm leveraging machine learning is introduced. The algorithm is further validated through engineering case studies to investigate semi-active control strategies in response to random seismic events. The results show that the seismic reliability of the machine learning-based semi-active control algorithm is significantly higher than that of the uncontrolled state for the same failure threshold under random seismic effects.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-74457-7