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相关概念视频

Eccentric Loading01:16

Eccentric Loading

319
Eccentric loading is a crucial concept in the study of structural engineering and mechanics, particularly when analyzing the stability and stress distribution in columns. Unlike centric loading, where the force is applied along the centroidal axis, causing uniform compression, eccentric loading occurs when a force is applied off-center. This off-center application introduces not only direct compressive stress but also bending stress, significantly influencing the column's behavior under...
319
Design of Columns under an Eccentric Load01:21

Design of Columns under an Eccentric Load

434
Designing columns to withstand eccentric loads is a critical aspect of structural engineering, ensuring structures can support off-center loads without failure. This design process must account for the additional normal stresses introduced by eccentric loading, which can significantly influence a column's stress distribution and overall stability. An eccentric load applied to a column induces normal stresses that can be conceptualized as a combination of stresses due to an equivalent...
434
Design of Columns under a Centric Load01:17

Design of Columns under a Centric Load

104
The design of columns under centric load is a fundamental aspect of structural engineering and is critical for ensuring the stability and integrity of structures. Euler's and Secant's formulas are central to understanding and calculating the critical load and deformation behaviors of columns, providing a basis for safe and effective structural design.
Euler's formula is applicable under the assumption that the column is a perfect, straight, homogenous prism, and it is operating...
104
Euler's Formula for Pin-Ended Columns01:21

Euler's Formula for Pin-Ended Columns

288
In structural engineering, the stability of columns under compressive axial loads is a critical consideration, described as buckling. A typical example involves a column PQ, which is pin-connected at both ends and subjected to a centric axial load F applied at one end, with a reaction force of F' = -F at the other end. Here, it is crucial to understand that when an applied load exceeds the critical load, buckling occurs as the system becomes unstable.
To calculate the critical load,...
288
Euler's Formula to Columns with Other End Conditions01:15

Euler's Formula to Columns with Other End Conditions

461
Euler's formula is very important in the field of structural engineering, providing a foundation for understanding the critical loading conditions of pin-ended columns. This formula links the modulus of elasticity, the moment of inertia of the cross-section, and the column's length, offering a precise calculation of the critical load at which a column is prone to buckling.
461
Eccentric Axial Loading in a Plane of Symmetry01:16

Eccentric Axial Loading in a Plane of Symmetry

161
Eccentric axial loading occurs when an axial load is applied away from the centroidal axis of a structural member. This scenario is common in engineering, where structural elements may not be directly aligned due to various design or functional requirements.
161

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使用自适应采样和ML技术在异常压缩下预测ECC-CES柱的强度.

Khaled Megahed1

  • 1Department of Structural Engineering, Mansoura University, PO BOX 35516, Mansoura, Egypt. k.megahed@mans.edu.eg.

Scientific reports
|January 8, 2025
PubMed
概括

工程混凝土复合材料 (ECC) 封闭的混凝土封装钢 (CES) 柱子显示出更好的性能. 机器学习模型准确地预测了它们的异常压缩能力,超过了当前的设计标准.

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:

  • 传统的混凝土钢 (CES) 柱子在柔性和性方面存在局限性.
  • 工程水泥复合材料 (ECC) 为结构应用提供了增强的材料性能.
  • 经ECC限制的CES (ECC-CES) 列呈现出一种具有卓越性能特性的新型复合材料.

研究的目的:

  • 开发一种创新的方法来预测ECC-CES柱的异常压力能力.
  • 使用自适应采样和机器学习 (ML) 来准确预测容量.
  • 为了比较ML模型的性能与既定的设计代码.

主要方法:

  • 开发和验证ECC-CES列的有限元素 (FE) 模型,包括材料和几何非线性.
  • 通过贝叶斯优化 (BO) 应用自适应采样来生成一个全面的FE数据库 (2,908个模型).
  • 六个ML模型 (GPR,CatBoost,LGBM等) 的培训和评估. 为了预测异常压缩能力.

主要成果:

  • 对FE模型的验证表明,与实验数据相比,FE模型具有很强的预测准确性.
  • 机器学习模型实现了高预测准确性,GPR,CatBoost和LGBM显示超过97%的样本在10%的误差范围内.
关键词:
适应性抽样采集方式在 CatBoost 模型中.异常压缩 异常压缩工程制造的混凝土复合材料.有限元素建模 有限元素建模机器学习 机器学习

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  • 在预测异常压缩能力方面,ML模型的性能明显优于EC4和AISC360设计标准.
  • 结论:

    • 机器学习,特别是GPR,CatBoost和LGBM,提供了一个非常准确的方法来预测ECC-CES列容量.
    • 适应性采样与ML的整合有效地产生了用于复杂结构分析的强大训练数据.
    • 虽然机器学习模型在准确性方面表现出色,但它们的可解释性需要进一步研究直接设计应用,这促使提出了一种新的设计方法.