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Geomechanics and Geoengineering
An International Journal
Volume 2, 2007 - Issue 2
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Original Articles

Prediction model of tunnel boring machine performance by ensemble neural networks

Zhiye Zhao School of Civil and Environmental Engineering, Nanyang Technological University, Nanyang Avenue, SingaporeCorrespondence[email protected]
,
Qiuming Gong Ecole Polytechnique Fédérales de Lausanne (EPFL), Rock Mechanics Laboratory, CH-1015, Lausanne, Switzerland; College of Architecture and Civil Engineering, Beijing University of Technology, Beijing, China
,
Yun Zhang School of Civil and Environmental Engineering, Nanyang Technological University, Nanyang Avenue, Singapore
&
Jian Zhao Ecole Polytechnique Fédérales de Lausanne (EPFL), Rock Mechanics Laboratory, CH-1015, Lausanne, Switzerland
Pages 123-128 | Received 09 Dec 2006, Published online: 05 Jun 2007

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Xu Li, Haibo Li, Saizhao Du, Liujie Jing & Pengyu Li. (2023) Cross-project utilisation of tunnel boring machine (TBM) construction data: a case study using big data from Yin-Song diversion project in China. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 17:1, pages 127-147.
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J. Morgenroth, K. Kalenchuk, L. Moreau-Verlaan, M. A. Perras & U. T. Khan. (2023) A novel long-short term memory network approach for stress model updating for excavations in high stress environments. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 17:1, pages 196-216.
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Seyed Mahdi Pourhashemi, Kaveh Ahangari, Jafar Hassanpour & Seyed Mosleh Eftekhari. (2022) TBM performance analysis in very strong and massive rocks; case study: Kerman water conveyance tunnel project, Iran. Geomechanics and Geoengineering 17:4, pages 1110-1122.
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Behrooz Keshtegar, Mahdi Hasanipanah, Troung Nguyen-Thoi, Saffet Yagiz & Hassan Bakhshandeh Amnieh. (2021) Potential efficacy and application of a new statistical meta based-model to predict TBM performance. International Journal of Mining, Reclamation and Environment 35:7, pages 471-487.
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Articles from other publishers (45)

Chao Song, Tengyuan Zhao, Ling Xu & Xiaolin Huang. (2024) Probabilistic prediction of uniaxial compressive strength for rocks from sparse data using Bayesian Gaussian process regression with Synthetic Minority Oversampling Technique (SMOTE). Computers and Geotechnics 165, pages 105850.
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Michał Juszczyk, Tomáš Hanák, Miloslav Výskala, Hanna Pacyno & Michał Siejda. (2023) Early Fast Cost Estimates of Sewerage Projects Construction Costs Based on Ensembles of Neural Networks. Applied Sciences 13:23, pages 12744.
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C. Gokceoglu, C. Bal & C. H. Aladag. (2023) Modeling of Tunnel Boring Machine Performance Employing Random Forest Algorithm. Geotechnical and Geological Engineering 41:7, pages 4205-4231.
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Haitao Long, Xiangqian Lu, Chunchi Ma, Tianbin Li, Wenjin Yan, Hang Zhang & Kunkun Dai. (2023) A dynamic learning method based on the Gaussian process for tunnel boring machine intelligent driving. Frontiers in Earth Science 11.
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Xin Yin, Xing Huang, Yucong Pan & Quansheng Liu. (2022) Point and interval estimation of rock mass boreability for tunnel boring machine using an improved attribute-weighted deep belief network. Acta Geotechnica 18:4, pages 1769-1791.
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Anshul Sindhwani, V. M. S. R. Murthy, Md. Raphique & A. K. Raina. (2023) Decoding rate of penetration of tunnel boring machine in Deccan Traps under varied geological and machine variables using response surface analysis. Bulletin of Engineering Geology and the Environment 82:3.
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Anshul Sindhwani, V. M. S. R. Murthy & Mohammad Raphique. (2022) Ground Characterization and TBM Performance Evaluation in Fractured Basaltic Terrain Near the Arabian Sea, India. Indian Geotechnical Journal 52:6, pages 1423-1434.
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Ibrahim Karatas & Abdulkadir Budak. (2022) Development and comparative of a new meta-ensemble machine learning model in predicting construction labor productivity. Engineering, Construction and Architectural Management.
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Hongbo Zhao, Lin Zhang, Jiaolong Ren, Meng Wang & Zhiqiang Meng. (2022) AdaBoost-Based Back Analysis for Determining Rock Mass Mechanical Parameters of Claystones in Goupitan Tunnel, China. Buildings 12:8, pages 1073.
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Daohong QiuKang FuYiguo XueYufan TaoFanmeng KongChenghao Bai. (2022) TBM Tunnel Surrounding Rock Classification Method and Real-Time Identification Model Based on Tunneling Performance. International Journal of Geomechanics 22:6.
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Anil Kumar Agrawal, V. M. S. R. Murthy, Somnath Chattopadhyaya & A. K. Raina. (2022) Prediction of TBM Disc Cutter Wear and Penetration Rate in Tunneling Through Hard and Abrasive Rock Using Multi-layer Shallow Neural Network and Response Surface Methods. Rock Mechanics and Rock Engineering 55:6, pages 3489-3506.
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Abolfazl Baghbani, Tanveer Choudhury, Susanga Costa & Johannes Reiner. (2022) Application of artificial intelligence in geotechnical engineering: A state-of-the-art review. Earth-Science Reviews 228, pages 103991.
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Candan Gokceoglu. (2021) Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey. SN Applied Sciences 4:1.
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Jian Zhou, Yingui Qiu, Shuangli Zhu, Danial Jahed Armaghani, Manoj Khandelwal & Edy Tonnizam Mohamad. (2021) Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization. Underground Space 6:5, pages 506-515.
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Fanzhen Meng, Louis Ngai Yuen Wong & Hui Zhou. (2021) Rock brittleness indices and their applications to different fields of rock engineering: A review. Journal of Rock Mechanics and Geotechnical Engineering 13:1, pages 221-247.
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Brian B Sheil, Stephen K Suryasentana, Michael A Mooney & Hehua Zhu. (2020) Machine learning to inform tunnelling operations: recent advances and future trends. Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction 173:4, pages 74-95.
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Andrea Rispoli, Anna Maria Ferrero & Marilena Cardu. (2020) From Exploratory Tunnel to Base Tunnel: Hard Rock TBM Performance Prediction by Means of a Stochastic Approach. Rock Mechanics and Rock Engineering 53:12, pages 5473-5487.
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Masoud Samaei, Masoud Ranjbarnia, Vahid Nourani & Masoud Zare Naghadehi. (2020) Performance prediction of tunnel boring machine through developing high accuracy equations: A case study in adverse geological condition. Measurement 152, pages 107244.
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B. Liu, R. Wang, G. Zhao, X. Guo, Y. Wang, J. Li & S. Wang. (2020) Prediction of rock mass parameters in the TBM tunnel based on BP neural network integrated simulated annealing algorithm. Tunnelling and Underground Space Technology 95, pages 103103.
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Bin Liu, Ruirui Wang, Zengda Guan, Jianbin Li, Zhenhao Xu, Xu Guo & Yaxu Wang. (2019) Improved support vector regression models for predicting rock mass parameters using tunnel boring machine driving data. Tunnelling and Underground Space Technology 91, pages 102958.
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Michał Juszczyk, Krzysztof Zima & Wojciech Lelek. (2019) FORECASTING OF SPORTS FIELDS CONSTRUCTION COSTS AIDED BY ENSEMBLES OF NEURAL NETWORKS. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 25:7, pages 715-729.
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Masoud Zare Naghadehi, Masoud Samaei, Masoud Ranjbarnia & Vahid Nourani. (2018) State-of-the-art predictive modeling of TBM performance in changing geological conditions through gene expression programming. Measurement 126, pages 46-57.
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Wei Sun, Maolin Shi, Jieling Li, Xin Ding, Lintao Wang & Xueguan Song. (2018) Surrogate-Based Multisource Sensitivity Analysis of TBM Driving System. Shock and Vibration 2018, pages 1-14.
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Giacomo Armetti, Maria Rita Migliazza, Federica Ferrari, Andrea Berti & Paolo Padovese. (2018) Geological and mechanical rock mass conditions for TBM performance prediction. The case of “La Maddalena” exploratory tunnel, Chiomonte (Italy). Tunnelling and Underground Space Technology 77, pages 115-126.
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Reza Mikaeil, Masoud Zare Naghadehi & Saleh Ghadernejad. (2017) An Extended Multifactorial Fuzzy Prediction of Hard Rock TBM Penetrability. Geotechnical and Geological Engineering 36:3, pages 1779-1804.
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Hadi Fattahi & Nima Babanouri. (2017) Applying Optimized Support Vector Regression Models for Prediction of Tunnel Boring Machine Performance. Geotechnical and Geological Engineering 35:5, pages 2205-2217.
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Limao Zhang, A.J. Antony Chettupuzha, Hongyu Chen, Xianguo Wu & Simaan M. AbouRizk. (2017) Fuzzy cognitive maps enabled root cause analysis in complex projects. Applied Soft Computing 57, pages 235-249.
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Hadi Fattahi & Ali Moradi. (2016) Risk Assessment and Estimation of TBM Penetration Rate Using RES-Based Model. Geotechnical and Geological Engineering 35:1, pages 365-376.
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M.C. Tonkins & J.S. Coggan. (2017) Characterization of Rock Fracturing for Vertical Boreability. Procedia Engineering 191, pages 112-118.
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Limao ZhangXianguo WuMiroslaw J. Skibniewski. (2016) Simulation-Based Analysis of Tunnel Boring Machine Performance in Tunneling Excavation. Journal of Computing in Civil Engineering 30:4.
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Huiyun Li & Erxia Du. (2016) Simulation of rock fragmentation induced by a tunnel boring machine disk cutter. Advances in Mechanical Engineering 8:6, pages 168781401665155.
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Hang-Lo Lee, Ki-Il Song & Gye-Chun Cho. (2016) Analysis on prediction models of TBM performance: A review. Journal of Korean Tunnelling and Underground Space Association 18:2, pages 245-256.
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Deniz Tumac & Cemal Balci. (2015) Investigations into the cutting characteristics of CCS type disc cutters and the comparison between experimental, theoretical and empirical force estimations. Tunnelling and Underground Space Technology 45, pages 84-98.
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Li Gao & Xi-bing Li. (2015) Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions. Journal of Central South University 22:1, pages 290-295.
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F.J. Macias, P.D. Jakobsen, Y. Seo & A. Bruland. (2014) Influence of rock mass fracturing on the net penetration rates of hard rock TBMs. Tunnelling and Underground Space Technology 44, pages 108-120.
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Mohammad Reza Moradi & Mohammad Ali Ebrahimi Farsangi. (2013) Application of the Risk Matrix Method for Geotechnical Risk Analysis and Prediction of the Advance Rate in Rock TBM Tunneling. Rock Mechanics and Rock Engineering 47:5, pages 1951-1960.
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Hanifi Copur, Hamit Aydin, Nuh Bilgin, Cemal Balci, Deniz Tumac & Can Dayanc. (2014) Predicting performance of EPB TBMs by using a stochastic model implemented into a deterministic model. Tunnelling and Underground Space Technology 42, pages 1-14.
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Ebrahim Ghasemi, Saffet Yagiz & Mohammad Ataei. (2013) Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic. Bulletin of Engineering Geology and the Environment 73:1, pages 23-35.
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Hadi Bejari & Jafar Khademi Hamidi. (2012) Simultaneous Effects of Joint Spacing and Orientation on TBM Cutting Efficiency in Jointed Rock Masses. Rock Mechanics and Rock Engineering 46:4, pages 897-907.
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C. Balci & D. Tumac. (2012) Investigation into the effects of different rocks on rock cuttability by a V-type disc cutter. Tunnelling and Underground Space Technology 30, pages 183-193.
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Jafar Khademi Hamidi, Kourosh Shahriar, Bahram Rezai & Jamal Rostami. (2011) Response by the authors to S. Yagiz discussion to the paper: J. Khademi Hamidi et al., Performance prediction of hard rock TBM using Rock Mass Rating (RMR) system [Tunnell. Undergr. Space Technol. 25 (2010) 333–345]. Tunnelling and Underground Space Technology 26:6, pages 795-797.
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Sze-Chun Lau, Ming Lu & Samuel T. Ariaratnam. (2010) Applying radial basis function neural networks to estimate next-cycle production rates in tunnelling construction. Tunnelling and Underground Space Technology 25:4, pages 357-365.
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Jafar Khademi Hamidi, Kourosh Shahriar, Bahram Rezai & Jamal Rostami. (2010) Performance prediction of hard rock TBM using Rock Mass Rating (RMR) system. Tunnelling and Underground Space Technology 25:4, pages 333-345.
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C. Balci. (2009) 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. Tunnelling and Underground Space Technology 24:4, pages 423-435.
Crossref
Q.M. Gong & J. Zhao. (2008) Response by the authors to R.M. Goktan discussion to the paper: Q.M. Gong and J. Zhao (2007). Influence of rock brittleness on TBM penetration rate in Singapore granite, Tunnelling and Underground Space Technology, Vol. 22, pp. 317–324. Tunnelling and Underground Space Technology 23:2, pages 217-218.
Crossref

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