Performance prediction of tunnel boring machine through developing a gene expression programming equation

被引:0
作者
Danial Jahed Armaghani
Roohollah Shirani Faradonbeh
Ehsan Momeni
Ahmad Fahimifar
M. M. Tahir
机构
[1] Amirkabir University of Technology,Department of Civil and Environmental Engineering
[2] Tarbiat Modares University,Department of Mining
[3] University of Tehran,School of Civil Engineering
[4] Lorestan University,Faculty of Engineering
[5] Universiti Teknologi Malaysia,UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction (ISIIC), Faculty of Civil Engineering
来源
Engineering with Computers | 2018年 / 34卷
关键词
Tunnel boring machine; Penetration rate; Gene expression programming; Multiple regression;
D O I
暂无
中图分类号
学科分类号
摘要
The prediction of tunnel boring machine (TBM) performance in a specified rock mass condition is crucial for any mechanical tunneling project. TBM performance prediction in accurate may reduce the risks related to high capital costs and scheduling for tunneling. This paper presents a new model/equation based on gene expression programming (GEP) to estimate performance of TBM by means of the penetration rate (PR). To achieve the aim of the study, the Pahang–Selangor Raw Water Transfer tunnel in Malaysia was investigated and the data related to field observations and laboratory tests were used in modelling of PR of TBM. A database (1286 datasets in total) comprising 7 model inputs related to rock (mass and material) properties and machine characteristics and 1 output (PR) was prepared to use in GEP modelling. To evaluate capability of the developed GEP equation, a multiple regression (MR) model was also proposed. A comparison between the obtained results has been done using several performance indices and the best equations of GEP and MR were selected. System results for the developed GEP equation based on coefficient of determination (R2) were obtained as 0.855 and 0.829 for training and testing datasets, respectively, while these values were achieved as 0.795 and 0.789 for the developed MR equation. Concluding remark is that the GEP equation is superior in comparison with the MR equation and it can be introduced as a new equation in the field of TBM performance prediction.
引用
收藏
页码:129 / 141
页数:12
相关论文
共 50 条
  • [31] A Gene Expression Programming Model for Predicting Tunnel Convergence
    Hajihassani, Mohsen
    Abdullah, Shahrum Shah
    Asteris, Panagiotis G.
    Armaghani, Danial Jahed
    APPLIED SCIENCES-BASEL, 2019, 9 (21):
  • [32] Reliability-Based Performance Optimization of Tunnel Boring Machine Considering Geological Uncertainties
    Wang, Lintao
    Sun, Wei
    Long, Yangyang
    Wang, Xu
    IEEE ACCESS, 2018, 6 : 19086 - 19098
  • [33] Prediction of geological composition using recurrent neural networks and shield tunnel boring machine data
    Pourhomayoun, Mohammad
    Mazari, Mehran
    Fisher, Luis
    Nagrecha, Kabir
    Rodriguez-Nikl, Tonatiuh
    Mooney, Michael
    Alavi, Ehsan
    CIVIL ENGINEERING AND ENVIRONMENTAL SYSTEMS, 2023, 40 (04) : 252 - 266
  • [34] Evaluation of Hard Rock Tunnel Boring Machine (TBM) Performance Using Stochastic Modeling
    Jafarshirzad, Peyman
    Ghasemi, Ebrahim
    Yagiz, Saffet
    Kadkhodaei, Mohammad Hossein
    GEOTECHNICAL AND GEOLOGICAL ENGINEERING, 2023, 41 (06) : 3513 - 3529
  • [35] Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data
    Sun, Wei
    Shi, Maolin
    Zhang, Chao
    Zhao, Junhong
    Song, Xueguan
    AUTOMATION IN CONSTRUCTION, 2018, 92 : 23 - 34
  • [36] Classification and prediction of rock mass drillability for a tunnel boring machine based on operational data mining
    Sun, Mingshe
    Chen, Song
    He, Huafei
    Wang, Wenzheng
    Song, Kezhi
    Lin, Xuebing
    FRONTIERS IN EARTH SCIENCE, 2024, 12
  • [37] Advancing Tunnel Boring Machine Performance Prediction in Massive and Highly Fractured Granite: Integrating Innovative Deep Learning and Block Model Techniques
    Monthanopparat, N.
    Tanchaisawat, T.
    GEOTECHNICAL ENGINEERING, 2024, 55 (03): : 26 - 34
  • [38] Prediction of the hydrodynamic performance and cavitation volume of the marine propeller using gene expression programming
    Mahmoodi, Kumars
    Ghassemi, Hassan
    Nowruzi, Hashem
    Shora, Mohammad Mahdi
    SHIPS AND OFFSHORE STRUCTURES, 2019, 14 (07) : 723 - 736
  • [39] A multi-channel decoupled deep neural network for tunnel boring machine torque and thrust prediction
    Yu, Honggan
    Qin, Chengjin
    Tao, Jianfeng
    Liu, Chengliang
    Liu, Quansheng
    TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY, 2023, 133
  • [40] A New System to Evaluate Comprehensive Performance of Hard-Rock Tunnel Boring Machine Cutterheads
    Zhu, Ye
    Sun, Wei
    Huo, Junzhou
    Meng, Zhichao
    CHINESE JOURNAL OF MECHANICAL ENGINEERING, 2019, 32 (01)