学报(中文)

基于刀盘扭转能量的土压平衡盾构刀具磨损分析

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  • 1. 南京理工大学 机械工程学院, 南京 210094; 2. 陆军工程大学 爆炸冲击防灾减灾国家重点实验室, 南京 210007
荣雪宁(1988-),男,博士,主要研究方向为地下空间开发.

网络出版日期: 2019-09-10

基金资助

国家自然科学基金(51608529),中国博士后科学基金(2018M640488)

Analysis of Tools Wear for Earth Pressure Balance Shield Based on Torque Energy of Cutterhead

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  • 1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; 2. State Key Laboratory of Disaster Prevention and Mitigation of Explosion and Impact, The Army Engineering University of PLA, Nanjing 210007, China

Online published: 2019-09-10

摘要

在卵石和中风化泥岩中掘进时,土压平衡盾构面临严重的刀具磨损.为合理组织换刀工作,需预计刀具的磨损情况.通过分析成都地铁20个盾构区间的部分掘进数据,以刀盘扭转能量作为刀具磨损模型的输入参数,对换刀数目与刀盘扭转能量的关系进行了拟合.在此基础上,增加泡沫添加剂体积,建立双参数预测模型,进一步优化刀具磨损的预测精度.结果表明:换刀数目与刀盘扭转能量正相关,与泡沫添加剂体积负相关;换刀数目与双参数间的回归关系具有统计显著性;卵石和中风化泥岩地层中的换刀数目都较好地符合双参数预测模型但不同地层中的拟合参数有所差异.双参数刀具磨损模型的预测精度显著优于基于掘进距离的单参数模型和JTS模型.

本文引用格式

荣雪宁,卢浩,王明洋,文祝,戎晓力,王振 . 基于刀盘扭转能量的土压平衡盾构刀具磨损分析[J]. 上海交通大学学报, 2019 , 53(8) : 965 -970 . DOI: 10.16183/j.cnki.jsjtu.2019.08.011

Abstract

Excessive wear of cutting tools has been reported in some earth pressure balance tunneling project in gravel and the intermediately weathered mudstone. In order to reasonably organize the changing work, the wear of the cutting tools should be predicted. Based on the operational data collected from 20 units of Chengdu Metro, the torque energy of cutterhead was chosen as the input parameter of cutter wear model. A relationship between the number of cutter changes and the torque energy was fitted. The volume of foaming liquid was then added to develop a multiple regression model, which can further improve the prediction precision of tools wear. The result shows that the number of cutter replaced increases with the increase of torque energy or the decrease of volume of foaming liquid. The relationship between the number of cutter replaced and the two input parameters is statistically significant. The number of cutter replaced for the gravel and the intermediately weathered mudstone both agree with the multiple regression model. For different soil types, different fitting parameters are observed. The multiple regression model gives more reasonable predictions than the model based on excavated length and the JTS model.

参考文献

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