[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]
点击复制

土仓压力与掘进参数相关性分析及预测模型()
分享到：

《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:

[1] 季凯, 吴宁林. 土压平衡盾构土仓压力的应用对比分析[J]. 中国水运（下半月）, 2021, 21（2）: 145-146.[2] 王志云, 李守巨, 于贺. 盾构机土仓压力平衡系统混合建模方法研究进展[J]. 水利水电技术, 2019, 50（S1）: 6-11.[3] 冯力, 许娇, 芦荣海, 等. 土压平衡盾构土仓压力设定及与地表沉降关系研究[J]. 市政技术, 2017, 35（4）: 127-130.[4] 王洪新, 傅德明. 土压平衡盾构掘进的数学物理模型及各参数间关系研究[J]. 土木工程学报, 2006, 39（9）: 86-90.[5] 上官子昌. 土压平衡盾构机密封舱压力控制机理模型及其实验研究[D].大连: 大连理工大学, 2011.[6] LIU X, SHAO C, MA H F, etal. Optimal earth pressure balance control for shield tunneling based on LS-SVM and PSO[J]. Automation in Construction, 2011, 20（4）: 321-327.[7] 李守巨, 曹丽娟. 盾构机土仓压力控制模型及其参数辨识[J]. 煤炭学报, 2012, 37（2）: 206-210.[8] 王洪新. 土压平衡盾构刀盘扭矩计算及其与盾构施工参数关系研究[J]. 土木工程学报, 2009, 42（9）: 109-113.[9] 李锟, 田管凤, 马宏伟, 等. 土压平衡盾构掘进参数相关性分析及预测模型[J]. 科学技术与工程, 2021, 21（9）: 3 814-3 821.[10] 王永军. 基于LSTM的深基坑开挖地表沉降预测研究[J]. 山西建筑, 2021, 47（14）: 74-75, 140.[11] FAZAKA S-ANCAIS, MODREA A, VLASE S. Using the stochastic gradient descent optimization algorithm on estimating of reactivity ratios[J]. Materials, 2021, 14（16）: 4764.[12] CHANDRIAH K K, NARAGANAHALLI R V. RNN/LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting[J]. Multimedia Tools and Applications, 2021, 80（17）: 26 145-26 159.[13] 杨凯弘, 张茜, 周思阳. 盾构工程实测数据时序性质分析与掘进速率预测[J]. 机械科学与技术, 2021, 40（6）: 835-839.

相似文献/References:

[1]叶建华,唐辉,罗奋翔,等.基于改进MobileNet的咖啡豆缺陷检测[J].福建工程学院学报,2023,21(03):257.[doi:10.3969/j.issn.1672-4348.2023.03.009]
　YE Jianhua,TANG Hui,LUO Fenxiang,et al.Coffee bean defect detection based on improved MobileNet[J].Journal of FuJian University of Technology,2023,21(01):257.[doi:10.3969/j.issn.1672-4348.2023.03.009]
[2]许煜濠,刘石坚,康朝明,等.三维深度学习网络的几何差异感知能力[J].福建工程学院学报,2023,21(06):592.[doi:10.3969/j.issn.1672-4348.2023.06.013]
　XU Yuhao,LIU Shijian,KANG Chaoming,et al.Geometric difference perception capabilities of 3D deep learning networks[J].Journal of FuJian University of Technology,2023,21(01):592.[doi:10.3969/j.issn.1672-4348.2023.06.013]
[3]郭宝椿,李佐勇,陈健,等.融合长短时记忆与图结构学习的水库水位预测[J].福建工程学院学报,2024,22(01):90.[doi:10.3969/j.issn.2097-3853.2024.01.013]
　GUO Baochun,LI Zuoyong,CHEN Jian,et al.Reservoir level prediction via integrating long short-term memory and graph structure learning[J].Journal of FuJian University of Technology,2024,22(01):90.[doi:10.3969/j.issn.2097-3853.2024.01.013]
[4]于明源,周景亮,曾绍锋,等.基于YOLOv8改进的轴承表面缺陷检测方法[J].福建工程学院学报,2024,22(03):280.[doi:10.3969/j.issn.2097-3853.2024.03.011]
　YU Mingyuan,ZHOU Jingliang,ZENG Shaofeng,et al.Improved bearing surface defect detection method based on YOLOv8[J].Journal of FuJian University of Technology,2024,22(01):280.[doi:10.3969/j.issn.2097-3853.2024.03.011]

更新日期/Last Update: 2022-02-25