一个基于LSTM的深度构成模型,用于描述具有不同粗度接口的混凝土-花岩复合材料的冲击特性
Huan Gao1, Yue Zhai2,3, Tienan Wang4
1School of Civil Engineering, Xi'an Shiyou University, Xi'an, 710065, China.
Scientific reports
|November 25, 2024
概括
研究混凝土-花岩复合材料发现,较高的关节粗度显著提高了抗冲击能力. 这项研究还开发了一种深度学习模型,用于预测压力下的复合行为.
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
- 计算力学 计算力学 计算力学
背景情况:
- 岩石工程结构通常涉及复合材料,如混凝土和周围岩石.
- 了解这些复合材料的动态机械性能和抗冲击性对于结构完整性至关重要.
- 接口特性,如粗度,在动态负载下显著影响复合材料的行为.
研究的目的:
- 研究具有不同接口粗度的混凝土-花岩复合材料的动态机械性能.
- 为了评估外层周围岩石复合结构的抗冲击性能.
- 探索深度学习模型对动态压力-应变关系的预测能力.
主要方法:
- 分裂霍普金森压力杆 (SHPB) 系统用于动态机械测试.
- 动态单轴压力强度和张变速率效应的实验分析.
- 开发和应用长短期记忆 (LSTM) 深度学习模型进行预测.
主要成果:
- 动态压力强度增加了20.55%,随着关节度系数 (JRC) 的增加.
- 联合研究中心增强了延展率效应,但减少了限制压力效应.
- 与BPNN和RF相比,LSTM模型在预测动态应力-应变关系方面表现出卓越的准确性.
结论:
- 接口粗度是提高混凝土-花岩复合材料抗冲击性能的一个关键因素.
- 深度学习,特别是LSTM,为建模复杂的材料行为提供了强大的工具.
- 这些发现有助于改善岩石工程应用中的设计和安全评估.
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