符号回归用于预测异常加载的混凝土填充钢管柱的强度
1Department of Structural Engineering, Mansoura University, PO BOX 35516, Mansoura, Egypt. k.megahed@mans.edu.eg.
Scientific reports
|January 24, 2025
概括
本研究引入了一种新的基于代码的符号回归 (C-SR) 模型,以准确预测混凝土填充钢管 (CFST) 柱的偏心强度. C-SR模型为结构设计提供可靠和可解释的预测.
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 (CFST) 柱子在现代建筑中非常重要,因为它们的高性能.
- 准确预测异常强度对于确保结构完整性和安全至关重要.
- 现有的方法包括理论模型和黑盒机器学习方法,每个方法都有局限性.
研究的目的:
- 开发一种混合模型,将设计代码规定与象征回归相结合,用于预测CFST异常强度.
- 与现有方法相比,提高预测准确性和可解释性.
- 为设计CFST结构的工程师提供一个强大的工具.
主要方法:
- 使用符号回归与结构设计规范条款相结合.
- 开发了一个全面的实验数据库,包括464个圆形CFST (CCFST) 和313个矩形CFST (RCFST) 列测试.
- 与EC4和AISC360标准以及各种机器学习技术对比开发的基于代码的符号回归 (C-SR) 模型.
主要成果:
- 在C-SR模型中,CCFST (预测与实际比率平均: 1.006,Cov: 0.117) 和RCFST (预测与实际比率平均: 0.997,Cov: 0.098) 的预测准确度都很高.
- 该模型提供了可解释的数学表达式,符合机械原理和代码规定.
- 该C-SR模型表现出相当于或超过既定标准和ML方法的强大性能.
结论:
- 开发的C-SR模型对于预测CFST柱的偏心强度是有效的.
- 该模型为结构工程师提供了一个可靠,可解释和实用的工具.
- 这种方法增强了在结构设计代码开发中的符号回归的应用.
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