针对双护盾TBM施工速度预测问题,以兰州市水源地建设工程输水隧洞双护盾TBM施工为背景,采用现场实测数据统计分析的方法,基于《水利水电工程地质勘察规范》(GB 50487—2008)的围岩分类HC评分值,研究了TBM净掘进速度、TBM利用率与围岩分类HC评分值的相关性,进而建立了双护盾TBM施工速度预测模型。结果表明:①TBM净掘进速度与HC值呈二次函数关系,相关性系数为0.84,随着HC值的降低,TBM净掘进速度呈现增加的趋势;②TBM利用率与围岩HC值呈二次函数关系,相关性系数为0.82,TBM利用率随着HC值的增加有先增大后减少的趋势;③围岩的HC值为41～46时,TBM的施工速度达到最高,日进尺可达到45 m以上,当HC值＜41时,TBM施工速度随着HC值的降低而降低,当HC值＞46时,TBM施工速度随着HC值的增加而降低;④预测施工速度与实际施工速度吻合较好,平均误差为5.2%,最大误差＜10%,说明预测模型可靠,可用于双护盾TBM施工速度预测。
Abstract
In the light of the HC evaluation system in the Code for Geologic Investigation of Water Resources and Hydropower Engineering, a model of predicting the advance rate of double shield Tunnel Boring Machine (TBM) is established based on analysing the correlations between the net penetration rate and utilization rate of TBM and the HC score through statistical analysis of field measured data. The double shield TBM construction of water conveyance tunnel of Lanzhou water source project is taken as a case study. Results reveal 1) a quadratic function relationship between net penetration rate and HC score with the correlation coefficient reaching 0.84. With the decrease of HC score, the net penetration rate shows an increasing trend. 2) There is also a quadratic function relationship between TBM utilization rate and HC score, with the correlation coefficient being 0.82. With the increase of HC score, the utilization rate increases first and then decreases. 3) When HC score ranges between 41 and 46, the advance rate of TBM peaks with the daily tunneling distance reaching 45 m. When HC score is smaller than 41, advance rate decreases along with the decline of HC score. When HC score is greater than 46, advance rate reduces with the increase of HC score. 4) The predicted advance rate is in good agreement with the actual construction speed, with an average error of 5.2% and a maximum error less than 10%, indicating that the prediction model is reliable and can be used to predict the advance rate of double shield TBM.
关键词
双护盾TBM /
围岩分类HC评分 /
利用率 /
净掘进速度 /
施工速度 /
预测模型
Key words
double shield TBM /
HC score of surrounding rock classification /
utilization rate /
net penetration rate /
advance rate /
prediction model
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 张镜剑, 傅冰骏. 隧道掘进机在我国应用的进展[J]. 岩石力学与工程学报, 2007, 26(2): 226-238.
[2] 杜立杰. 中国TBM施工技术进展、挑战及对策[J]. 隧道建设, 2017, 37(9): 1063-1075.
[3] 齐梦学.我国TBM法隧道工程技术的发展、现状及展望[J]. 隧道建设(中英文),2021,41(11):1964-1979.
[4] 梁晋平, 李文全, 史国珍. 双护盾全断面掘进机在万家寨引黄工程中的应用[J]. 现代隧道技术, 2001, 38(4): 27-31, 36.
[5] 赵海雷, 陈 馈, 周建军, 等. 引松供水4标TBM连续穿越灰岩的施工技术研究[J]. 隧道建设, 2017, 37(3): 354-362.
[6] 李卫国, 孔凡彬. 锦屏二级水电站引水隧洞工程TBM施工[J]. 四川水力发电, 2015, 34(增刊2): 5-8.
[7] 石 曲. 影响开敞式TBM掘进效率的因素及设备改进建议[J]. 建筑机械化, 2012, 33(增刊2): 82-87.
[8] 刘明月,杜彦良,麻士琦.地质因素对TBM掘进效率的影响[J].石家庄铁道学院学报,2002,15(4):40-43.
[9] 杨庆辉. 锦屏TBM掘进效率的影响因素分析及思考[J]. 国防交通工程与技术, 2012, 10(1): 51-54.
[10]陈 浩. TBM掘进效率与围岩相关性研究[D]. 成都: 西南交通大学, 2010.
[11]龚秋明, 卢建炜, 魏军政, 等. 基于岩体分级系统(RMR)评估预测TBM利用率研究[J]. 施工技术, 2018, 47(5): 92-98, 127.
[12]王 旭, 李 晓, 李守定. 关于用岩体分类预测TBM掘进速率AR的讨论[J]. 工程地质学报, 2008, 16(4): 470-475.
[13]曹瑞琅, 王玉杰, 陈 晨, 等. TBM净掘进速度预测模型发展现状及参数分析[J]. 水利水电技术, 2019, 50(8): 96-105.
[14]罗 华. 基于线性回归和深度置信网络的TBM性能预测研究[D]. 杭州: 浙江大学, 2018.
[15]ROSTAMI J. Performance Prediction of Hard Rock Tunnel Boring Machines (TBMS) in Difficult Ground[J]. Tunnelling and Underground Space Technology, 2016, 57: 173-182.
[16]DELISIO A, ZHAO J, EINSTEIN H H. Analysis and Prediction of TBM Performance in Blocky Rock Conditions at the Ltschberg Base Tunnel[J]. Tunnelling and Underground Space Technology, 2013, 33: 131-142.
[17]BIENIAWSKI Z T. Determining Rock Mass Deformability: Experience from Case Histories[J]. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 1978, 15(5): 237-247.
[18]BARTON N. Some New Q-Value Correlations to Assist in Site Characterisation and Tunnel Design[J]. International Journal of Rock Mechanics and Mining Sciences, 2002, 39(2): 185-216.
[19]GB/T 50218—2014,工程岩体分级标准[S]. 北京: 中国计划出版社, 2015.
[20]GB 50487—2008,水利水电工程地质勘察规范[S]. 北京: 中国计划出版社, 2008.
[21]杨继华, 梁国辉, 曹建锋, 等. 兰州市水源地建设工程输水隧洞TBM1施工段关键技术研究[J]. 现代隧道技术, 2019, 56(2): 10-17.
[22]石怡安,苏 凯,王美斋,等.TBM引水隧洞衬砌管片形式研究[J].水电能源科学,2016,34(4):103-106.
[23]宁向可, 姜 桥, 田 鹏. 国产双护盾TBM在兰州市水源地建设工程中的应用[J]. 隧道建设, 2017, 37(增刊1): 149-154.
[24]王 健, 王瑞睿, 张欣欣, 等. 基于RMR岩体分级系统的TBM掘进性能参数预测[J]. 隧道建设, 2017, 37(6): 700-707.
基金
国家自然科学基金项目(41972270);黄河勘测规划设计研究院有限公司自主研究开发项目(2020-ky04,2022-ky03)
PDF(2160 KB)
