Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Scientific Reports

DOI: 10.1038/s41598-024-74457-7

Authors: Zhongyuan Xiao, Jianguo Xu, Li Wang, Liang Huang

Institution: Zhengzhou University

Language: English

Topic: Vibration Control and Rheological Fluids

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,590