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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Author Correction: Advanced predictive machine and deep learning models for round-ended CFST column.

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相关实验视频

Updated: May 27, 2025

Curtain Flow Column: Optimization of Efficiency and Sensitivity
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针对圆端CFST列的先进预测机器和深度学习模型.

Feng Shen1,2, Ishan Jha3, Haytham F Isleem4,5

  • 1College of Civil Engineering, Huaqiao University, Xiamen, 361021, China.

Scientific reports
|February 20, 2025
PubMed
概括
此摘要是机器生成的。

机器学习模型准确地预测了混凝土填充钢管式 (CFST) 柱体容量. CatBoost实现了最高的准确性,超过了结构分析的传统方法和深度学习模型.

关键词:
轴负载预测的轴承负载预测用混凝土填充的钢管式柱子.深度学习架构是一种深度学习架构.机器学习模型的机器学习模型在SHAP分析中,分析结构工程应用 结构工程应用

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相关实验视频

Last Updated: May 27, 2025

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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:

  • 封闭式柱子,特别是混凝土填充的钢管式柱子 (CFST),由于其强度和效率,在现代基础设施中至关重要.
  • 准确预测轴承载能力 (Pcc) 对于确保结构完整性和优化设计至关重要.
  • 现有的分析解决方案往往难以捕捉CFST列的复杂,非线性行为.

研究的目的:

  • 开发和评估数据驱动的方法,特别是机器学习 (ML) 和深度学习 (DL) 模型,用于预测CFST列的轴承载能力.
  • 将这些ML/DL模型的性能与已建立的分析解决方案进行基准测试.
  • 识别影响CFST列承载能力的关键输入特征.

主要方法:

  • 利用了一个广泛的数据集,包括200个CFST柱实验测试.
  • 评估了六种模型:LightGBM,XGBoost,CatBoost (ML) 和深度神经网络 (DNN),卷积神经网络 (CNN),长短期记忆 (LSTM) (DL).
  • 在特征重要性分析中使用了夏普利添加式解释 (SHAP).

主要成果:

  • CatBoost模型表现出卓越的预测准确性,其RMSE为396.50 kN,R2为0.932.
  • ML模型通常表现优于DL模型,DNN实现了496.19 kN的RMSE和0.958的R2,而LSTM表现不佳 (RMSE: 2010.46 kN).
  • 截面宽度被确定为最重要的积极预测因素,而列长是关键的负面影响因素.

结论:

  • 数据驱动的模型,特别是CatBoost,为CFST列容量提供了强大而准确的预测,超越了传统的分析方法.
  • 开发的模型为特征的重要性提供了可解释的见解,有助于工程判断.
  • 一个用户友好的Python界面使这些先进的预测工具在结构设计中的实用实时应用成为可能.