一个新的综合智能算法预测模型用于道表面定居点.
You Wang1, Fang Dai2, Ruxue Jia2
1School of Civil Engineering, Central South University, Changsha, 410075, China. ywang1920@csu.edu.cn.
Scientific reports
|June 17, 2023
概括
这项研究引入了一种新的方法,用于预测盾道施工期间的地面沉积. EMD-CASSA-ELM模型提高了预测准确度和速度,这对建筑安全至关重要.
科学领域:
- 地质技术工程 地质技术工程
- 土木工程 土木工程是指土木工程.
- 计算智能是一种计算智能.
背景情况:
- 盾牌道建设带来了土壤沉积的风险,需要准确的安全预测.
- 现有的预测方法可能缺乏实时监控的准确性和速度.
研究的目的:
- 开发一个先进的预测模型,用于由盾构引发的地面结算.
- 提高表面沉积预测的准确性和效率,以加强安全监测.
主要方法:
- 经验模式分解 (EMD) 用于将结算数据分成趋势和波动组件.
- 混沌自适应搜索算法 (CASSA) 用于优化极端学习机器 (ELM) 参数.
- 整合EMD,CASSA和ELM,创建一个混合预测模型 (EMD-CASSA-ELM).
主要成果:
- 用立方混沌映射和适应因子增强的CASSA算法优化了ELM权重和值.
- 通过分析分解的组件,EMD-CASSA-ELM模型准确地预测和重建表面沉积.
- 与传统的ELM相比,元启发式优化的ELM模型在预测准确度上有10.70%的改进.
结论:
- 该EMD-CASSA-ELM模型显著提高了表面沉积预测的准确性和速度.
- 这种智能预测方法为屏蔽道施工中的安全监测提供了一种新方法.
- 自动化和快速地表沉降预测是该领域的一个关键发展趋势.
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