基于机器学习的智能半活性控制算法和地震可靠性的研究.
Zhongyuan Xiao1, Jianguo Xu1, Li Wang1
1School of Water Conservancy and Transportation, Zhengzhou University, No.100 Kexue Road, High-Tech Developing District, ZhengZhou, 450001, He Nan, China.
Scientific reports
|November 28, 2024
概括
一个新的基于机器学习的半活性控制算法提高了水道结构的地震可靠性. 与传统方法相比,这种智能方法可以提高对随机地震事件的稳定性和控制效率.
科学领域:
- 土木工程 土木工程是指土木工程.
- 结构工程 结构工程
- 地震工程是地震工程.
背景情况:
- 现有的半活性控制算法缺乏稳定性.
- 目前的地震可靠性评估方法的概括性有限.
- 水道结构需要强大的地震控制策略.
研究的目的:
- 为地震可靠性评估提出一种新的概率密度演变方法.
- 利用机器学习开发一个智能半活性控制算法.
- 评估在随机地震事件下对水道进行半活性控制的有效性.
主要方法:
- 对于地震可靠性分析的概率密度演变.
- 机器学习用于开发智能半活性控制算法.
- 工程案例研究以验证控制策略.
主要成果:
- 拟议的方法使控制有效性的全面评估成为可能.
- 基于机器学习的半活性控制算法显著提高了地震可靠性.
- 在随机地震效应下,与不受控制状态相比,具有较高的地震可靠性.
结论:
- 新的概率密度演变方法增强了地震可靠性评估.
- 机器学习驱动的半活性控制为水道提供了卓越的性能.
- 智能控制策略对于减轻结构中的地震风险是有效的.
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