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2018 2

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TBM 1

TBM 刀盘设计 1

TBM 效率 1

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三维激发极化法 1

不良地质体 1

凿岩速率指数 1

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比能法在渤海海峡隧道TBM 施工中的应用分析

高攀,邹翀

《中国工程科学》 2013年第15卷第12期页码 73-79

摘要：

拟建的渤海海峡隧道采用隧道掘进机（TBM）施工极具挑战性，其中关键之一就是TBM的掘进速率预测问题。在Clem Jones 隧道施工过程中，相关研究人员运用比能法对TBM的可掘性进行了分析及预测，从后期的掘进情况来看比能法的预测结果与实际施工吻合得较好。本文在Clem Jones 隧道和渤海海峡隧道对比分析的基础上，认为运用比能法预测TBM的可掘性同样适用于渤海海峡隧道的TBM施工。

关键词： TBM 比能凿岩速率指数掘进速率

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Clogging of slurry-shield tunnel-boring machine drives in sedimentary soft rock: A case study

《结构与土木工程前沿（英文）》 doi: 10.1007/s11709-023-0984-8

摘要： This paper presents a case study of the clogging of a slurry-shield tunnel-boring machine (TBM) experienced during tunnel operations in clay-rich argillaceous siltstones under the Ganjiang River, China. The clogging experienced during tunneling was due to special geological conditions, which had a considerably negative impact on the slurry-shield TBM tunneling performance. In this case study, the effect of clogging on the slurry-shield TBM tunneling performance (e.g., advance speed, thrust, torque, and penetration per revolution) was fully investigated. The potential for clogging during tunnel operations in argillaceous siltstone was estimated using an existing empirical classification chart. Many improvement measures have been proposed to mitigate the clogging potential of two slurry-shield TBMs during tunneling, such as the use of an optimum cutting wheel, a replacement cutting tool, improvements to the circulation flushing system and slurry properties, mixed support integrating slurry, and compressed air to support the excavation face. The mechanisms and potential causes of clogging are explained in detail, and the contributions of these mitigation measures to tunneling performance are discussed. By investigating the actual operational parameters of the slurry-shield TBMs, these mitigation measures were proven to be effective in mitigating the clogging potential of slurry-shield TBMs. This case study provides valuable information for slurry-shield TBMs involving tunneling in clay-rich sedimentary rocks.

关键词： slurry-shield TBM geological investigation clogging argillaceous siltstone TBM performance mitigation measures

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超特长隧洞TBM 集群施工风险管控技术 Article

邓铭江

《工程（英文）》 2018年第4卷第1期页码 112-122 doi: 10.1016/j.eng.2017.07.001

摘要：主洞划分了18 台隧洞掘进机（TBM）掘进段和34 个钻爆法施工段，施工掌子面多达91 个。该工程不仅对建设管理、风险防控、安全高效施工提出前所未有的挑战，同时也对TBM 及其配套设备的设计、制造、运维提出了更高的要求。

关键词：超特长隧洞地质灾害 TBM 集群施工质量安全风险防控

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QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

《结构与土木工程前沿（英文）》 2023年第17卷第1期页码 25-36 doi: 10.1007/s11709-022-0908-z

摘要： In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.

关键词： tunnel boring machine control parameter optimization quantum particle swarm optimization artificial neural network tunneling energy efficiency

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关于硬岩隧道掘进机刀盘设计的进一步研究

Jamal Rostami, Soo-Ho Chang

《工程（英文）》 2017年第3卷第6期页码 892-904 doi: 10.1016/j.eng.2017.12.009

摘要：

在隧道项目中，隧道掘进机（TBM）能否顺利施工取决于TBM 系统所有部件（从刀具到后备系统）能否正常运转，并取决于全部机车车辆的配合。而在整个TBM 系统中，刀盘是影响TBM 施工效率最为关键的因素。刀盘的设计影响到掘削效率、刀盘平衡、刀具使用寿命、主轴承/ 齿轮箱维护、出渣效果、面刀和边刀/ 排渣铲斗的磨损情况。

关键词： TBM 刀盘设计刀盘布置盘形滚刀刀具布置形式 TBM 效率

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异形掘进机设计制造关键技术研究及工程应用

李建斌

《工程（英文）》 2017年第3卷第6期页码 905-914 doi: 10.1016/j.eng.2017.12.002

摘要：

随着地下空间开发进程的加快，越来越多的大型掘进机（TBM）应用于地下工程开挖。目前，常规TBM 已无法完全满足地下工程对断面形式和空间利用率的追求，异形TBM 成为理想断面开挖设备成为市场新的增长点。本文首先介绍了异形TBM 的技术特点及发展现状，然后结合典型工程与工法创新，针对矩形顶管和马蹄形TBM 开展了盾体结构设计优化、多刀盘联合开挖、异形管片拼装、黄土地层渣土改良等多项关键技术研究，为异形断面TBM 的设计、制造和施工提供了一套可行的解决方案。相关工程实践表明，采用针对性设计、制造的异形TBM，在施工进度、地表沉降控制和空间利用等方面具有巨大优势。

关键词：异形TBM 矩形顶管机马蹄形TBM 地下空间开发

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TBM施工隧洞不良地质超前预报实践——以吉林省中部城市引松供水工程为例 Article

李术才, 聂利超, 刘斌

《工程（英文）》 2018年第4卷第1期页码 131-137 doi: 10.1016/j.eng.2017.12.010

摘要：

由于隧洞掘进机（TBM）施工具有快速、安全、高效的显著优势, 采用TBM 施工的隧洞越来越多。然而，TBM 施工对地层的适应性较差，在施工过程中遭遇的诸如溶洞、断层或破碎带等不良地质，往往会造成突水突泥、塌方、卡机等灾害，严重危及施工安全。针对TBM 施工隧洞的复杂环境，本文以吉林引松供水工程TBM 施工隧洞为依托，综合运用地质分析、三维激发极化与地震超前地质预报方法，提出了适用于TBM 隧洞施工期的综合超前地质预报技术体系，对TBM 施工隧洞掌子面前方的溶洞避免了隧洞突水突泥、塌方、卡机灾害的发生，对于TBM施工隧洞不良地质超前预报与TBM 安全高效掘进具有一定借鉴意义。

关键词： TBM 隧洞综合超前预报地质分析三维激发极化法地震法不良地质体

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中国隧道及地下工程修建技术

王梦恕,谭忠盛

《中国工程科学》 2010年第12卷第12期页码 4-10

摘要：

主要介绍了我国隧道与地下工程的设计理论及方法、钻爆法隧道施工技术、盾构隧道施工技术和TBM（tunnel boring machine）施工技术等

关键词：隧道地下工程设计理论钻爆法盾构与TBM法

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渤海海峡隧道TBM 施工风险评估及关键风险对策探讨

李达,洪开荣,谭忠盛

《中国工程科学》 2013年第15卷第12期页码 95-100

摘要：分析了影响风险等级的关键风险为隧道掘进机（TBM）主轴承制造风险、突水突泥风险和施工通风风险，并根据这些风险的特征提出了风险对策建议和尚需解决的关键问题，为渤海海峡隧道的决策提供参考，同时也为跨海隧道的规划

关键词：渤海海峡隧道层次分析法模糊评价风险评估风险对策

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Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel

Zaobao LIU; Yongchen WANG; Long LI; Xingli FANG; Junze WANG

《结构与土木工程前沿（英文）》 2022年第16卷第4期页码 401-413 doi: 10.1007/s11709-022-0823-3

摘要： Real-time dynamic adjustment of the tunnel bore machine (TBM) advance rate according to the rock-machine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction. This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network (TCN), based on TBM construction big data. The prediction model was built using an experimental database, containing 235 data sets, established from the construction data from the Jilin Water-Diversion Tunnel Project in China. The TBM operating parameters, including total thrust, cutterhead rotation, cutterhead torque and penetration rate, are selected as the input parameters of the model. The TCN model is found outperforming the recurrent neural network (RNN) and long short-term memory (LSTM) model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two. The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment. On the contrary, the influence of the cutterhead rotation and total thrust is moderate. The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.

关键词： hard rock tunnel tunnel bore machine advance rate prediction temporal convolutional networks soft computing construction big data