使用混合机器学习技术预测钢质等角结构部件的曲折损伤
Nang Xuan Ho1,2, Tien-Thinh Le3,4, The-Hung Dinh5,2
1Faculty of Vehicle and Energy Engineering, Phenikaa University, Yen Nghia, Ha Dong, Hanoi, 12116, Vietnam.
Scientific reports
|February 8, 2025
概括
这项研究使用混合人工智能 (AI) 模型预测了钢角度成员的曲损伤. 与传统方法相比,粒子群集优化-人工神经网络 (PSO-ANN) 方法可以提高预测准确度.
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:
- 曲是钢结构构件的关键故障模式.
- 准确预测曲损伤对于结构安全和设计至关重要.
- 预测曲的现有方法在复杂的场景中可能缺乏精度.
研究的目的:
- 开发和验证一种混合人工智能 (AI) 模型,用于预测钢铁等角部件的曲损伤.
- 通过机器学习和元启发性优化,提高曲负载预测的准确性.
- 将拟议的混合模型与传统技术的性能进行比较.
主要方法:
- 构建一个综合的数据库,从钢铁等角部件的压缩试验中构建数据库.
- 开发一种混合的人工神经网络 (ANN) 和粒子集群优化 (PSO) 模型 (PSO-ANN).
- 校准和验证PSO-ANN模型使用几何和材料属性,包括不完美.
主要成果:
- 混合PSO-ANN模型的预测准确性明显高于传统的方法,如缩放联梯度 (SCG).
- 关键绩效指标显示有实质性的改善:RMSE从0.141降至0.055,MAE从0.108降至0.042,R2从0.749增加至0.959.
- 部分依赖 (PD) 分析被用来解释PSO-ANN模型的"黑子"性质.
结论:
- 混合PSO-ANN模型提供了一种优越的方法,用于预测钢铁等角成员的曲损伤.
- 这种人工智能驱动的方法为结构分析和设计提供了更可靠,更准确的工具.
- 该研究强调了将机器学习与对复杂工程问题的优化集成的潜力.
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