四肢CFST格子柱的地震性能:实验和ANN预测
Juan Chen1, Jun-Jie He2, Zhi Huang3,4
1School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China.
Scientific reports
|September 27, 2025
概括
这项研究测试了混凝土填充的钢管状 (CFST) 格子柱,并使用了优化的人工神经网络 (ANN) 进行地震性能预测. 量子增强的子搜索算法 (QMESSA) 准确预测了列行为,改善了结构分析.
科学领域:
- 结构工程 结构工程
- 地震性能分析的分析
- 计算力学 计算力学 计算力学
背景情况:
- 填充混凝土的钢管状 (CFST) 格子柱是关键的结构部件.
- 准确的地震性能预测对于结构安全和设计至关重要.
- 现有的人工神经网络 (ANN) 模型在预测歇斯底里曲线方面存在局限性.
研究的目的:
- 为了研究四肢CFST格子柱在水平低循环反转负荷下的地震性能.
- 为了提高地震性能的预测准确度,使用一个优化的ANN.
- 通过将量子计算与子搜索算法 (SSA) 集成来开发一种新的预测模型.
主要方法:
- 实验测试具有不同细度和轴负载比的四肢CFST格子柱.
- 开发一个ANN模型来预测地震性能.
- 使用用量子计算 (QMESSA) 增强的小子搜索算法 (SSA) 优化ANN权重和值.
主要成果:
- 实验结果显示出明显的hysteresis曲线形状 (弓,反向S,),表明不同的塑料容量和故障模式.
- QMESSA有效地优化了ANN,导致负载位移歇斯底里曲线的准确预测.
- 来自QMESSA的预测损害变量与实验数据和传统损害模型有很强的一致性.
结论:
- 四肢CFST格子柱的地震性能受到细度和轴承负载比率的显著影响.
- 在优化ANN以预测CFST格子列的地震行为方面,QMESSA表现出卓越的性能.
- 拟议的QMESSA提供了一种可靠和准确的方法来评估这些结构元素的地震性能.
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