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The dynamic modulus of elasticity assesses how a concrete structure deforms under impact or dynamic loads. It is typically higher than the static modulus of elasticity, measured under slow, steady loading conditions.
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The quantity that describes the deformation of a body under stress is known as strain. Strain is given as a fractional change in either length, volume, or geometry under tensile, volume (also known as bulk), or shear stress, respectively, and is a dimensionless quantity. The strain experienced by a body under tensile or compressive stress is called tensile or compressive strain, respectively. In contrast, the strain experienced under bulk stress and shear stress is known as volume and shear...
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使用机器学习技术的新型和高度准确的静态模块模型.

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本研究引入了一种先进的机器学习方法,使用自适应性神经模糊推理系统 (ANFIS) 准确预测碳化合物储库中的静态模量 (Es). 在确定岩石类型和改进石油工程应用方面,ANFIS模型表现出卓越的性能.

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科学领域:

  • 石油地质科学 地质科学
  • 机器学习应用 机器学习应用
  • 岩石机械学 岩石机械学

背景情况:

  • 静态模量 (Es) 对于石油计算至关重要.
  • 现有的ES预测模型缺乏精度,范围有限.
  • 精确的ES测定对于地质评估和水库工程至关重要.

研究的目的:

  • 开发和评估新的机器学习模型来预测静态模量 (Es).
  • 将自适应神经模糊推理系统 (ANFIS) 与现有模型的性能进行比较.
  • 评估ANFIS在基于预测ES的岩石类型识别方面的能力.

主要方法:

  • 利用了自适应神经模糊推断系统 (ANFIS) 和其他机器学习技术.
  • 采用散体形成密度 (RHOB),剪切波速度 (DTs) 和压缩波速度 (DTc) 作为预测变量.
  • 在1853个碳化合物储岩石样本的多样化数据集上训练和验证模型.

主要成果:

  • 在ANFIS模型中,平均绝对百分比相对误差 (AAPRE) 为5.1%,相关系数 (R) 为0.9602.
  • 与其他机器学习方法相比,ANFIS显示出更高的预测准确度和更快的决策速度.
  • 趋势分析证实,增加RHOB会增加Es,而增加DTc和DTs会减少Es.

结论:

  • 安菲斯是以高精度预测静态模量 (Es) 的最佳模型.
  • 该ANFIS模型准确地捕捉了岩石属性和Es之间的物理关系.
  • 这项研究为地质评估和石油工程应用提供了重大进展.