超长隧洞开挖掘进机的合理配置

深理超长隧洞在开挖时,其施工机械的合理设置是非常重要的。隧洞掘进机台数的确定既要考虑施工成本、施工工期的可行性,又要考虑其他因素的影响,要合理分析掘进机的性价比,针对不同工程特性,找出每台掘进机的最优经济开挖长度,从而合理确定掘进机的施工台数。     

TBM掘进隧洞回填固结灌浆施工的探讨

豆砾石回填灌浆和固结灌浆在引黄工程南干线长达90kmTBM施工中得到成功的应用。文中介绍了引黄工程南干线4^#-7^#隧洞豆砾石回填灌浆、特殊地质洞段的固结灌浆的灌浆设备、材料、浆液配比、压力、工艺等诸方面的现场施工情况。     

深埋长隧洞不良地质洞段TBM施工对策

全断面双护盾岩石掘进机施工隧洞,开挖与衬砌平行作业,具有施工速度快、效率高、施工安全等特点。但对于深埋长隧洞来说,由于地质条件复杂,TBM施工就会遇到一些困难。为此,对不良地质洞段的施工对策进行了探讨,以供参考。     

敞开式TBM隧洞通风系统布置及除尘效果研究

为了研究敞开式TBM施工通风布置方案并提高通风除尘效果,以引汉济渭秦岭输水隧洞TBM工程为依托,利用数值计算软件FLUENT对不同送风风速,不同送风管位置下的洞内除尘效果进行了计算研究,并将模拟计算与现场实测结果对比,优化了施工通风系统布置。研究结果表明:粉尘质量浓度从拱架作业区至主控室逐渐增大,随着到掌子面的距离增加,粉尘浓度降低;送风风速越大,洞内粉尘危险浓度区域越广,敞开式TBM内人员集中作业区的粉尘质量浓度越小;送风管距掌子面越远,人员集中作业区粉尘浓度越高,送风管布置在距掌子面15 m时,作业区粉尘浓度最低。优化方案将送风风管和除尘风管分别布置在距掌子面15 m和25 m,送风风速和除尘风速为40 m/s和15 m/s时,作业区粉尘质量浓度可控制在2 mg/m~3规范限值以下。     

ＴＢＭ隧道下穿市政桥梁施工变形规律研究

针对ＴＢＭ不同角度下穿既有结构,对既有结构产生的扰动问题,依托青岛地铁１号线海泊桥站到小村庄站区间ＴＢＭ隧道下穿市政桥梁工程,利用ＦＬＡＣ３Ｄ数值模拟分析软件与现场施工监测相结合的方法,建立相应的三维地质力学模型,研究双线ＴＢＭ隧道施工对既有桥梁结构的受力特征与变形规律,揭示ＴＢＭ隧道下穿市政桥梁施工变形规律。计算结果表明:ＴＢＭ下穿会导致既有桥面板向上隆起,左线隧道开挖引起的桥面板隆起值要远远大于右线开挖;当左线隧道开挖到桥梁跨中时,桥头与桥尾隆起量最大,最大值约４．６３ｍｍ;新建隧道下穿既有结构尽量采用小角度下穿,避免既有结构发生大变形。     

希腊雅典调水二期工程

雅典调水二期工程因连续大旱,仓卒上马.黏土心墙坝为引水枢纽中的主要建筑物,坝址调水比例66%～75%,隧洞最大埋深1 250 m,复理石地层,施工机械既有开敞式TBM,又有双护盾TBM,分别采用现浇钢筋混凝土及单层管片衬砌.人字坡压力输水洞不均匀输水.     

突出矿井煤巷快速综掘

针对突出矿井半煤巷使用综掘机一直是国内煤炭行业的一大难题。总结了在突出矿井,且巷道支护又是架棚巷道的情况下,使用掘进机成效显著,提高了掘进进尺,缓解了采掘接替紧张的矛盾。针对现场实际,又对掘进机的个别部件进行了改进,提高工效,提出了突出矿井使用掘进机一些亟待解决的问题。     

Development of TSACO and application to Chinese HCCB TBM cooling system

In this study, a thermal-hydraulic and safety analysis code (TSACO) for helium cooling system has been developed using Fortran 90 language, and the simulation has been performed for the cooling system of the Chinese helium cooled ceramic breeder test blanket module (CH HCCB TBM). The semi-implicit finite difference technique was adopted for the solution of the dynamic behavior of helium cooling system. Furthermore, a detailed illustration of the numerical solution for heat structures and critical model was presented. The code was verified by the comparison of RELAP5 code with the same initial condition, boundary condition, heat transfer and flow friction models. The TBM inlet/outlet temperatures and pressure drop were obtained and the results simulated by TSACO were shown in good agreement with those by RELAP5. Thereafter, the design basis accident in-vessel loss of coolant accident (LOCA), was investigated for the CH HCCB TBM cooling system. The critical flow model was also verified by comparing with RELAP5 code. The results indicated that the TBM can be cooled down effectively. The vacuum vessel (VV) pressure and the mass of helium spilled into the VV maintained below the design limits with a large margin.     

一种基于TBM掘进参数的现场岩石强度快速估算模型

基于隧道掘进机(tunnel boring machine, TBM)现场实际掘进参数与岩石强度的相关性,提出一种现场岩石强度快速估算模型。该模型详细分析了开敞式TBM现场实际掘进参数的趋势,建立了其与岩石单轴抗压强度(uniaxial compressive strength, UCS)的拟合关系式;根据回归系数R²排序:0.761 2(贯入度指数)> 0.759 5(推力)> 0.691 5(掘进比能)>0.598 6(扭矩)> 0.561 5(贯入度),确定采用贯入度指数(field penetration index, FPI)快速估算岩石强度UCS。与以往岩石强度经验模型对比,该估算模型更具针对性和适用性。研究成果为TBM工程岩石强度的快速估算提供了一种新的切实可行的思路。     

Application of artificial neural networks to the prediction of tunnel boring machine penetration rate

Rate of penetration of a Tunnel Boring Machine(TBM)in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project.This paper presents the results of a study into the application of an Artificial Neural Network(ANN)technique for modeling the penetration rate of tunnel boring machines.A database,including actual,measured TBM penetration rates,uniaxial compressive strengths of the rock,the distance between planes of weakness in the rock mass and rock quality designation was established.Data collected from three different TBM projects(the Queens Water Tunnel,USA,the Karaj-Tehran water transfer tunnel,Iran,and the Gilgel Gibe Ⅱ hydroelectric project,Ethiopia).A five-layer ANN was found to be optimum,with an architecture of three neurons in the input layer,9,7 and 3 neurons in the first,second and third hidden layers,respectively,and one neuron in the output layer.The correlation coefficient determined for penetration rate predicted by the ANN was 0.94.