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基于机器学习的测量和预测,通过盾牌道引发的地面定居点,在复合层中穿过现有道的盾牌道
Mei Dong1, Mingzhe Guan1, Kuihua Wang1
1Research Center of Coastal and Urban Geotechnical Engineering, Zhejiang University, Hangzhou 310058, China.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
这项研究介绍了一种基于粒子群优化的双向长短期记忆 (Bi-LSTM) 神经网络,用于在盾牌道中准确地预测地面定位. 优化的Bi-LSTM模型显著提高了预测准确性和稳定性,即使数据有限.
科学领域:
- 地质技术工程 地质技术工程
- 道工程工程 道工程
- 计算智能是一种计算智能.
背景情况:
- 传统的地面结算预测方法在复合层中缺少对盾牌道的准确性.
- 准确的定居点预测对于城市道建设的安全性和稳定性至关重要.
研究的目的:
- 开发一个高精度的动态预测模型,用于在小样本条件下的地面结算.
- 提高屏蔽道地下交叉路口的定居预测的准确性和稳定性.
主要方法:
- 提出了一个基于粒子群优化 (PSO) 的双向长期短期记忆 (Bi-LSTM) 神经网络.
- 比较了PSO优化的长短期内存 (LSTM),门式循环单元 (GRU) 和Bi-LSTM模型.
- 分析了使用Pearson相关系数进行模型优化的线性关系.
主要成果:
- 与LSTM和GRU相比,PSO优化的Bi-LSTM模型表现出优异的预测性能.
- 该模型在预测地面沉积时实现了高准确性和稳定性.
- 皮尔森相关性分析告知了模型优化,进一步增强了预测能力.
结论:
- 在复杂的道方案中,PSO-Bi-LSTM模型为精确的地面沉积预测提供了显著的进步.
- 这种方法对小样本条件有效,解决了传统方法的局限性.
- 该研究验证了PSO优化的深度学习在地质工程应用中的有效性.
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