基于物理测试和机器学习技术,预测砂纹理地球膜接口的温度依赖的长期爬行机械反应
Zhiming Chao1,2,3, Haoyu Wang3, Hanwen Hu4
1Shanghai Estuarine and Coastal Science Research Center, Shanghai 201201, China.
Materials (Basel, Switzerland)
|September 28, 2023
概括
评估沙子-地质膜接口的爬行对于工程至关重要. 机器学习模型,特别是反向传播人工神经网络 (BPANN),准确地预测爬行行为,温度是最重要的因素.
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:
- 沙地地膜接口的爬行机械反应对于工程应用至关重要.
- 性能受温度和应力条件的影响.
- 准确的评估是可靠的基础设施设计所需的.
研究的目的:
- 评估沙地地膜接口的长期爬行机械性能.
- 为了评估温度,正常压力和爬行剪压的影响.
- 为了比较不同的机器学习算法的性能来进行预测.
主要方法:
- 使用自主开发的温度控制的大接口剪切装置进行了长期爬行剪切测试.
- 从物理测试结果构建了一个数据库.
- 应用并比较了反向传播人工神经网络 (BPANN),支持矢量机 (SVM) 和极端学习机 (ELM) 算法.
主要成果:
- 基于统计标准 (RMSE,CC,WI,MAPE) 的BPANN模型显示出优异的预测性能.
- 温度被确定为对爬行界面机械性能最有影响的因素,其次是时间.
- 使用最佳模型的灵敏度分析提供了对影响因素的见解.
结论:
- 机器学习,特别是BPANN,提供了一种可靠的方法来预测沙地膜接口的爬行.
- 了解温度的影响对于确保地质膜安装设施的运行安全至关重要.
- 这些发现支持使用地球膜的地质工程项目的安全设计和运行.
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