实验测试和PSO增强的神经网络,用于对H段钢材成员的冲击故障分析
Pengcheng Chen1, Shuwen Bu2, Lin Wang3
1College of Civil Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Materials (Basel, Switzerland)
|July 12, 2025
概括
本研究引入了一种PSO-MLP模型,用于预测侧冲击负载下H段钢件的损坏,与其他方法相比,提供更高的准确性和稳定性.
科学领域:
- 结构工程 结构工程
- 计算力学 计算力学 计算力学
- 材料科学 材料科学 材料科学
背景情况:
- H段钢件在建筑结构中至关重要,易受意外冲击负荷的影响.
- 准确预测侧面冲击造成的损伤对于结构安全和设计至关重要.
研究的目的:
- 开发一个强大的预测模型,用于对受到侧面冲击负荷的H段钢件的损坏.
- 为了优化多层感知子 (MLP) 模型,使用粒子群优化 (PSO) 来提高预测准确度.
主要方法:
- 在侧冲击下对H段钢件的实验测试.
- 开发和应用一个结合的粒子群集优化-多层感知器 (PSO-MLP) 模型.
- 与随机森林 (RF) 和支持矢量机器 (SVM) 模型进行比较分析.
主要成果:
- PSO-MLP模型准确地预测了受到侧面冲击的H束柱的损坏.
- 与射频和SVM模型相比,PSO-MLP显示出更高的预测准确性和稳定性.
- 分析确定了影响冲击性能的关键特征,包括冲击位置,轴向压缩比,横截面尺寸和冲击角度.
结论:
- 该PSO-MLP模型提供了一个可靠的工具来评估H段钢件的冲击性能.
- 了解各种参数的影响对于设计抗意外冲击的弹性钢结构至关重要.
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