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使用ANN模型预测岩石的单轴压力强度:包括孔隙性,压力波速度和施密特数据
Panagiotis G Asteris1, Maria Karoglou2, Athanasia D Skentou1
1Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR 14121, Athens, Greece.
Ultrasonics
|May 23, 2024
概括
这项研究引入了一个人工神经网络 (ANN),以准确预测岩石.
科学领域:
- 地质技术工程 地质技术工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 不受限制的压力强度 (UCS) 对岩石工程至关重要.
- 传统的UCS测试通常是不切实际的,特别是对于历史建筑.
- 非破坏性测试 (NDT) 提供了替代方案,但需要复杂的相关性.
研究的目的:
- 开发一个新的人工神经网络 (ANN) 模型.
- 为了同时将UCS与施密特子反弹数,压缩波速度和开放有效孔隙性相关联.
- 为完整的岩石材料提供准确和高效的UCS估计.
主要方法:
- 利用人工神经网络 (ANN) 进行预测建模.
- 集成了三个非破坏性测试指数:施密特子反弹数,压缩波速度和开放有效孔隙度.
- 为实际的工程应用生成了轮图.
主要成果:
- 与现有方法相比,拟议的ANN模型在UCS预测中表现出更高的准确性.
- 该模型有效地将UCS与多个NDT索引相关联.
- 生成的轮图为工程师提供了宝贵的工具.
结论:
- 该ANN方法为UCS估计提供了一种简化和准确的方法.
- 这种综合方法可以提高完整岩石的工程评估.
- 这些发现有助于在地质技术和地质应用中的决策.
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