基于PSO-BP神经网络的盾构刀具配置研究

doi:10.3901/JME.2018.10.167

机械工程学报 ›› 2018, Vol. 54 ›› Issue (10): 167-172.doi: 10.3901/JME.2018.10.167

基于PSO-BP神经网络的盾构刀具配置研究

牛江川¹, 韩利涛¹, 李素娟², 郭京波¹, 刘进志¹

1. 石家庄铁道大学机械工程学院石家庄 050043;
2. 石家庄铁道大学信息科学与技术学院石家庄 050043

收稿日期:2017-08-28 修回日期:2018-02-05 出版日期:2018-05-20 发布日期:2018-05-20
通讯作者: 牛江川(通信作者),男,1977年出生,博士,副教授,硕士研究生导师。主要研究方向为计算机辅助设计、机械系统动力学。E-mail:menjc@163.com
基金资助:
国家自然科学基金（51275321）、国家863计划（2012AA041803）和河北省教育厅自然科学青年基金（QN2014151）资助项目。

Research on Shield Cutting Tool Configuration Based on PSO-BP Neural Network

NIU Jiangchuan¹, HAN Litao¹, LI Sujuan², GUO Jingbo¹, LIU Jinzhi¹

1. School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043;
2. School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043

Received:2017-08-28 Revised:2018-02-05 Online:2018-05-20 Published:2018-05-20

摘要/Abstract

摘要： 盾构刀具在盾构机掘进过程中起着关键的作用，其配置选型是否合理决定着工程的成败。为了对盾构刀具进行合理的配置，根据盾构刀具的配置原则，针对盾构刀具配置的地质适用性，在粒子群优化算法（Particle swarm optimization，PSO）与神经网络算法（Back propagation，BP）的基础上提出智能配置方法。建立地质条件与盾构刀具类型之间的关系模型，并利用成功的盾构施工案例作为样本数据对该模型进行训练，训练后可以利用模型智能推荐盾构刀具配置方案。利用工程案例进行测试分析，将测试结果与实际配置方案进行对比，并与BP神经网络测试结果进行比较。测试结果表明，基于PSO-BP神经网络算法不但能够很好地实现盾构刀具配置方案的合理推荐，并且在计算精度和训练时间两个方面PSO-BP神经网络算法比BP神经网络算法都有显著提高。

关键词: BP神经网络, 盾构刀具, 粒子群优化算法, 智能选型

Abstract: Shield cutting tools play a key role in the process of shield machine tunneling, and its reasonable configuration selection affects the success of the project. For the reasonable configuration and the geological applicability of shield cutting tools, an intelligent configuration method based on PSO-BP neural network hybrid algorithm is put forward, where the configuration principle of shield cutting tools is considered. The successful shield construction cases are used as sample data to train the relationship model, which is established between geological conditions and the types of shield cutting tools. And the trained model can achieve intelligent recommendation for the configuration scheme of shield cutting tools. The trained model is tested by engineering case, and the test result is compared with the actual configuration scheme. The test results show that, the PSO-BP neural network algorithm can not only achieve the reasonable recommendation on configuration scheme of shield cutting tools, but also it has significant improvement compared with the BP neural network algorithm in two aspects of calculation accuracy and training time.

Key words: BP neural network, intelligent selection, particle swarm optimization, shield cutting tool

中图分类号:

U455

牛江川, 韩利涛, 李素娟, 郭京波, 刘进志. 基于PSO-BP神经网络的盾构刀具配置研究[J]. 机械工程学报, 2018, 54(10): 167-172.

NIU Jiangchuan, HAN Litao, LI Sujuan, GUO Jingbo, LIU Jinzhi. Research on Shield Cutting Tool Configuration Based on PSO-BP Neural Network[J]. Journal of Mechanical Engineering, 2018, 54(10): 167-172.

参考文献

[1] XIE Haibo,LIU Zhibin,YANG Huayong,et al. Pressure regulation for earth pressure balance control on shield tunneling machine by using adaptive robust control[J]. Chinese Journal of Mechanical Engineering,2016,29(3):598-606.
[2] 夏毅敏,卞章括,胡承欢,等. 复合式土压平衡盾构机刀盘性能综合评价方法[J]. 机械工程学报,2014,50(21):1-9. XIA Yimin,BIAN Zhangkuo,HU Chenghuan,et al. Performance comprehensive evaluation of composite earth pressure balanced shield machine cutter head[J]. Journal of Mechanical Engineering,2014,50(21):1-9.
[3] 夏毅敏,周喜温,刘玉江,等. 某型土压平衡盾构刀盘布刀规律研究[J]. 湘潭大学自然科学学报,2009,31(4):92-96. XIA Yimin,ZHOU Xiwen,LIU Yujiang,et al. Study on cutters layout rules of EPB shield cutter-head[J]. Natural Science Journal of Xiangtan University,2009,31(4):92-96.
[4] 杨新安,张业炜,邱龑,等. 盾构机反交错双螺旋线刀具布置形式[J]. 同济大学学报,2013,41(8):1263-1268. YANG Xin'an,ZHANG Yewei,QIU Yan,et al. Shield machine cutters layout with reverse cross double spirals pattern[J]. Journal of Tongji University,2013,41(8):1263-1268.
[5] 蒲毅,刘建琴,郭伟,等. 土压平衡盾构机刀盘刀具布置方法研究[J]. 机械工程学报,2011,47(15):161-168. PU Yi,LIU Jianqin,GUO Wei,et al. Research on cutting tool layout method of earth pressure balance shield[J]. Journal of Mechanical Engineering,2011,47(15):161-168.
[6] 郭京波,王旭东,郑丽堃,等. 基于多目标遗传算法的复合式盾构刀盘刀具布置优化[J]. 隧道建设,2017,37(4):517-521. GUO Jingbo,WANG Xudong,ZHENG Likun,et al. Optimization of layout of disc cutter of composite shield based on multipurpose genetic algorithm[J]. Tunnel Construction,2017,37(4):517-521.
[7] 王飞,袁大军,董朝文,等. 盾构直接切削大直径群桩的刀具配置研究[J]. 土木工程学报,2013,46(12):127-135. WANG Fei,YUAN Dajun,DONG Chaowen,et al. Study on cutter configuration for directly shield cutting of large-diameter piles[J]. China Civil Engineering Journal,2013,46(12):127-135.
[8] 周刘刚,袁大军,吴俊,等. 富水高强交互复合岩层盾构施工关键技术研究[J]. 施工技术,2016,45(Suppl.2):127-132. ZHOU Liugang,YUAN Dajun,WU Jun,et al. Research on key technologies of shield construction in intrusive and mixed strata of high strength and rich water[J]. Construction Technology,2016,45(Suppl.2):127-132.
[9] 何小新,吴庆鸣. 全断面岩石掘进机智能选型方案生成系统[J]. 中国机械工程,2007,18(6):682-685. HE Xiaoxin,WU Qingming. Intelligent selection program generation system in full face rock tunnel boring machine[J]. Chinese Mechanical Engineering,2007,18(6):682-685.
[10] 黄德镛,胡运权,陈孝华,等. 基于模糊推理和神经网络的采矿方法智能决策系统[J]. 有色金属,2002,54(2):104-107. HUANG Deyong,HU Yunquan,CHEN Xiaohua,et al. Mining method intelligent decision system based on fuzzy reasoning and neural network[J]. Non-ferrous Metals,2002,54(2):104-107.
[11] 蒋新宇. 基于特征的刀具智能选取及其数据管理技术研究[D]. 杭州:浙江大学,2003. JIANG Xinyu. Features tool intelligent selection and data management technology[D]. Hangzhou:Zhejiang University,2003.
[12] 刘赟. 基于特征的刀具管理及智能推荐技术研究[D]. 南昌:南昌大学,2009. LIU Yun. Features tool management and intelligent recommendation technology research[D]. Nanchang:Nanchang University,2009.
[13] 周喜温. 土压平衡式复合盾构刀盘的刀具优化配置研究[D]. 长沙:中南大学,2010. ZHOU Xiwen. EPB Shield composite cutter tool optimal allocation[D]. Changsha:Central South University,2010.
[14] ROBBINS R J. Machine tunnelling in the twenty-first century[J]. Tunnelling & Underground Space Technology Incorporating Trenchless Technology Research,1987,2(2):147-154.
[15] 吴艳华,刘德全,盛少琴. 盾构机刀具选型及布置[J]. 广东造船,2013,32(1):54-56. WU Yanhua,LIU Dequan,SHENG Shaoqin. Analysis of the selection and collocation of the cutter head[J]. Guangdong Shipbuilding,2013,32(1):54-56.
[16] BALCI C. Correlation of rock cutting tests with field performance of a TBM in a highly fractured rock formation:A case study in Kozyatagi-Kadikoy metro tunnel,turkey[J]. Tunnelling & Underground Space Technology,2009,24(4):423-435.
[17] CHO J W,JEON S,YU S H,et al. Optimum spacing of TBM disc cutters:A numerical simulation using the three-dimensional dynamic fracturing method[J]. Tunnelling & Underground Space Technology,2010,25(3):230-244.
[18] 艾永冠,朱卫东,闫冬,等. 基于PSO-BP神经网络的股市预测模型[J]. 计算机应用,2008,28(5):105-108. AI Yongguan,ZHU Weidong,YAN Dong,et al. Stock market forecast model based on PSO-BP neural network[J]. Computer Applications,2008,28(5):105-108.
[19] 纪震,廖惠连,吴青华. 粒子群算法及应用[M]. 北京:科学出版社,2009. JI Zhen,LIAO Huilian,WU Qinghua. Particle swarm optimization and application[M]. Beijing:Science Press,2009.
[20] 陶海龙,李小平,张胜召,等. 基于混合智能算法的铁路运量预测研究[J]. 铁道运输与经济,2011,33(9):78-82. TAO Hailong,LI Xiaoping,ZHANG Shengzhao,et al. Prediction of railway traffic based on hybrid intelligent algorithm[J]. Railway Transport and Economy,2011,33(9):78-82.

基于PSO-BP神经网络的盾构刀具配置研究

Research on Shield Cutting Tool Configuration Based on PSO-BP Neural Network

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