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

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Abstract

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 (R 2) 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.

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Acknowledgements

The authors would like to extend their sincere gratitude to the Pahang–Selangor Raw Water Transfer Project Team, especially to Ir. Dr. Zulkeflee Nordin, Ir. Arshad, the contractor and consultant groups for facilitating this study. In addition, the authors wish to express their appreciation to Universiti Teknologi Malaysia for supporting this study and making it possible.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Amirkabir University of Technology, 15914, Tehran, Iran

    Danial Jahed Armaghani & Ahmad Fahimifar

  2. Department of Mining, Tarbiat Modares University, 14115-143, Tehran, Iran

    Roohollah Shirani Faradonbeh

  3. School of Civil Engineering, University of Tehran, 14155-6619, Tehran, Iran

    Ehsan Momeni

  4. Faculty of Engineering, Lorestan University, 68151-44316, Khorram Abad, Iran

    Ehsan Momeni

  5. UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction (ISIIC), Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    M. M. Tahir

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  1. Danial Jahed Armaghani
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Correspondence to Danial Jahed Armaghani.

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Jahed Armaghani, D., Faradonbeh, R.S., Momeni, E. et al. Performance prediction of tunnel boring machine through developing a gene expression programming equation. Engineering with Computers 34, 129–141 (2018). https://doi.org/10.1007/s00366-017-0526-x

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