使用机器学习技术预测 CFST 柱子的轴向压缩能力
Khaled Megahed1, Nabil Said Mahmoud1, Saad Elden Mostafa Abd-Rabou2
1Department of Structural Engineering, Mansoura University, Mansoura, Egypt.
Scientific reports
|February 4, 2024
概括
机器学习模型,包括高斯过程回归和符号回归,准确地预测混凝土填充钢管柱 (CFST) 的压力强度. 这些模型提供了可靠的设计工具,超越了结构工程应用的传统方法和现有代码.
科学领域:
- 结构工程 结构工程
- 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:
- 混凝土填充钢管柱 (CFST) 由于其高承载能力和柔性,在建筑中广泛使用.
- 现有的设计代码在CFST列的容量预测中存在不一致性,导致设计不确定性.
- 传统的回归方法很难准确地建模CFST柱体特性与压力强度之间的复杂关系.
研究的目的:
- 使用机器学习开发精确的CFST柱的压力强度预测模型.
- 解决目前设计代码和传统回归分析在预测CFST柱强度方面的局限性.
- 为各种CFST列类型提出来自机器学习模型的实际设计方程.
主要方法:
- 编制一个实验数据库,包括1316个CFST柱标本 (圆形,矩形,双皮).
- 使用几何和材料属性的高斯过程回归 (GPR) 和符号回归 (SR) 模型的实现.
- 引入一个无维的"强度指数",以提高模型性能,并与已建立的代码和其他ML算法进行比较 (PSVR,ANN,XGBoost,CatBoost,Random Forest,LightGBM).
主要成果:
- CatBoost (CATB),GPR,粒子群集优化支持向量回归 (PSVR) 和XGBoost (XGB) 模型在预测CFST柱强度方面表现出卓越的准确性和可靠性.
- 符号回归 (SR) 提供了适用于不同CFST列类型的简单,实用的设计方程.
- 使用沙普利增材解释 (SHAP) 的特征分析确定了断面细度比率和混凝土强度作为影响压力强度指数负面影响的关键因素.
结论:
- 开发的机器学习模型 (GPR,SR) 和衍生方程提供了可靠和准确的预测,用于压力强度的柱塞CFST列.
- 这些先进的计算工具可以帮助结构工程师克服与传统设计代码相关的不确定性.
- 这些发现为影响CFST柱性能的关键参数提供了宝贵的见解,增强了结构设计实践.
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