使用机器学习预测圆纤维增强聚合物混凝土钢双皮柱的轴承负载能力
Focai Yu1, Haytham F Isleem2, Walaa J K Almoghayer3
1School of Art and Design, Yunnan Light and Textile Industry VocationalCollege, Kunming City, 650300, Yunnan Province, China.
Scientific reports
|April 15, 2025
概括
人工智能 (AI) 模型准确预测混合列的负载能力. 极端梯度提升 (XGBoost) 和随机森林 (RF) 显示出卓越的性能,混凝土核心面积是关键因素.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
背景情况:
- 混合圆双皮管状柱 (DSTC) 提供先进的结构性质.
- 有限的实验数据阻碍了对这些复合结构的全面分析.
- 准确预测最终承载能力和应变对于结构设计至关重要.
研究的目的:
- 研究人工智能 (AI) 的应用,以预测混合圆式DSTC的性能.
- 开发和验证最终承载能力和应变的准确预测模型.
- 确定影响这些列结构行为的关键参数.
主要方法:
- 实现的机器学习 (ML) 模型:基因表达编程 (GEP),人工神经网络 (ANN),随机森林 (RF),自适应增强 (ADB) 和极端梯度增强 (XGBoost).
- 开发并验证了一个非线性有限元素 (FE) 模型,以生成112点的数据集.
- 进行了参数研究,对混凝土强度,钢管尺寸和FRP厚度进行了变化.
主要成果:
- 与其他AI技术相比,XGBoost和RF模型显示出更高的预测准确性 (R2,RMSE,MAE).
- 沙普利添加式扩展 (SHAP) 确定了混凝土核心面积是对承载能力最有影响的因素.
- 不受限制的混凝土强度和FRP特性也显著影响柱子性能.
结论:
- 人工智能,特别是XGBoost和RF,提供了一种可靠的方法来预测混合圆DSTC的轴承容量.
- 开发的FE模型和AI方法克服了数据稀缺问题.
- 一个用户界面平台促进了结构工程设计中的实际AI应用.
相关概念视频
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