一种混合PSO-FFNN方法,用于优化地震设计和精确的结构响应预测在钢铁抗冲动框架中
1College of Finance and Economics, Yinchuan University of Science and Technology, Yinchuan, Ningxia, China.
PloS one
|June 2, 2025
概括
这项研究优化了钢结构的地震设计,使用粒子群优化 (PSO) 和前神经网络 (FFNN). 综合方法增强了抗震能力,并大大减少了用于准确预测结构反应的计算时间.
科学领域:
- 结构工程 结构工程
- 计算力学 计算力学 计算力学
- 地震工程的工程是地震工程.
背景情况:
- 钢结构在建筑中普遍存在,但在地震事件期间在接时刻连接中面临脆弱断裂的挑战.
- 现有的地震设计方法需要优化,以提高钢结构在动态负载下的柔性和可靠性.
研究的目的:
- 为钢结构的抗震设计优化结构参数.
- 提出一种有效的计算技术,以提高抗震性和可靠性.
主要方法:
- 利用粒子群优化 (PSO) 来定义地震设计的结构特征.
- 采用前神经网络 (FFNN) 来预测结构反应和非常规的设计方法.
- 在地震负荷下设计和分析了一种现实的钢铁抗冲动框架 (MRF) 结构.
主要成果:
- 该PSO+FFNN模型在预测结构反应方面表现出高准确性,MAPE和RRMSE值较低.
- 为FFNN模型实现了2.4分钟的竞争性计算时间.
- 与WCFBP-RB方法相比,拟议的模型显示了各种地震参数的更高的预测准确性.
结论:
- 综合的PSO和FFNN方法有效地优化了钢结构的抗震设计.
- 该FFNN模型显著减少计算时间,同时保持高预测精度.
- 这种方法提高了钢结构的抗震性,可靠性和精度.
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