长期短期记忆网络用于预测弹性 稳定基材模块受湿干循环的影响
Mohammad A Al-Zubi1, Mahmood Ahmad2,3,4, Shahriar Abdullah5
1Department of Mechanical Engineering, Hijjawai Faculty for Engineering, Yarmouk University, Irbid, 21163, Jordan.
Scientific reports
|November 13, 2024
概括
一个新的长短期记忆 (LSTM) 模型准确地预测了在湿干周期 (WDC) 下稳定基材的弹性模量 (MR). 这种先进的AI方法为路面设计的传统测试方法提供了更有效的替代方案.
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
- 人工智能的人工智能
背景情况:
- 弹性模块 (MR) 对于机械-实证路面设计至关重要.
- 对于MR的传统动态三轴测试是昂贵和耗时的.
- 为MR开发高效的预测模型是必不可少的.
研究的目的:
- 开发一种新的长短期记忆 (LSTM) 网络模型,用于预测MR.
- 用一个全面的实验室数据集来评估模型的性能.
- 分析各种输入参数对MR预测的影响.
主要方法:
- 利用了704个记录的数据集,其中包括湿干周期 (WDC),化学成分比,密度-湿度比 (DMR),偏差应力 (σd) 和限制应力 (σ3) 等参数.
- 实施的LSTM网络用于MR预测.
- 在敏感性分析中使用了夏普利添加剂解释 (SHAP).
主要成果:
- LSTM模型实现了高精度,在训练中R值为0.995,在测试中为0.980.
- LSTM的表现优于现有的模型,如支向量的回归和最小平方.
- 灵敏度分析发现DMR是最重要的参数, σd是最不重要的参数.
结论:
- LSTM 网络提供了一种高度准确和高效的方法来预测稳定基材的MR.
- 开发的模型可以帮助优化路面设计和材料选择.
- 了解参数意义可以提高预测路面工程模型的可靠性.
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