[1]成晓元,凌静秀,黄继辉,等.土仓压力与掘进参数相关性分析及预测模型[J].福建工程学院学报,2022,20(01):13-18.[doi:10.3969/j.issn.1672-4348.2022.01.003]
　CHENG Xiaoyuan,LING Jingxiu,HUANG Jihui,et al.Correlation analysis and prediction model of chamber earth pressure and excavation parameters[J].Journal of FuJian University of Technology,2022,20(01):13-18.[doi:10.3969/j.issn.1672-4348.2022.01.003]
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土仓压力与掘进参数相关性分析及预测模型()
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《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第20卷
期数:
2022年01期
页码:
13-18
栏目:
出版日期:
2022-02-25

文章信息/Info

Title:
Correlation analysis and prediction model of chamber earth pressure and excavation parameters
作者:
成晓元; 凌静秀; 黄继辉; 吴勉
福建工程学院机械与汽车工程学院
Author(s):
CHENG Xiaoyuan;  LING Jingxiu;  HUANG Jihui;  WU Mian
School of Mechanical and Automotive Engineering, Fujian University of Technology
关键词:
LSTM(Long Short-Term Memory Network); 深度学习; 土仓压力; 相关性分析
Keywords:
LSTM;  deep learning;  soilsilo pressure;  correlation analysis
分类号:
TH113.1
DOI:
10.3969/j.issn.1672-4348.2022.01.003
文献标志码:
A
摘要:
提出一种基于长短期神经网络的深度学习预测模型，依托现场数据对土仓压力进行预测。结果表明，在5个可控因素的基础上，增加与土仓压力具有相关关系的不可控因素作为输入，评价指标平均绝对误差、均方误差分别降低了0.901%、0.021%，校正后的决定系数提高了16%，为土仓压力的精准预测和设定提供了借鉴。
Abstract:
A deep learning prediction model is proposed based on long-term and short-term neural networks to predict chamber earth pressure based on field data. Research results show that on the basis of 5 controllable factors, adding uncontrollable factors related to chamber earth pressure as input, the average absolute error and mean square error of evaluation index have been reduced by 0.901% and 0.021% respectively. The corrected coefficient of determination is increased by 16%, which provides a reference for the accurate prediction and setting of the chamber earth pressure.

参考文献/References:

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更新日期/Last Update: 2022-02-25