IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i6p1423-d1098135.html
   My bibliography  Save this article

Intelligent Analysis of Construction Costs of Shield Tunneling in Complex Geological Conditions by Machine Learning Method

Author

Listed:
  • Xiaomu Ye

    (PowerChina Huadong Engineering Corporation Limited, Hangzhou 311122, China)

  • Pengfei Ding

    (Hangzhou City Infrastructure Management Center, Hangzhou 310026, China)

  • Dawei Jin

    (Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University, Nanjing 210024, China)

  • Chuanyue Zhou

    (PowerChina Huadong Engineering Corporation Limited, Hangzhou 311122, China
    Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University, Nanjing 210024, China)

  • Yi Li

    (Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University, Nanjing 210024, China)

  • Jin Zhang

    (Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University, Nanjing 210024, China)

Abstract

The estimation of construction costs for shield tunneling projects is typically based on a standard quota, which fails to consider the variation of geological parameters and often results in significant differences in unit cost. To address this issue, we propose a novel model based on a random forest machine learning procedure for analyzing the construction cost of shield tunnelling in complex geological conditions. We focus specifically on the unit consumption of grease, grouting, labor, water, and electricity. Using a dataset of geotechnical parameters and consumption quantities from a shield tunneling project, we employ KNN and correlation analysis to reduce the input dataset dimension from 17 to 6 for improved model accuracy and efficiency. Our proposed approach is applied to a shield tunneling project, with results showing that the compressive strength of geomaterial is the most influential parameter for grease, labor, water, and electricity, while it is the second most influential for grouting quantity. Based on these findings, we calculate the unit consumption and cost of the tunnelling project, which we classify into three geological categories: soil, soft rock, and hard rock. Comparing our results to the standard quota value, it is found that the unit cost of shield tunneling in soil is slightly lower (6%), while that in soft rock is very close to the standard value. However, the cost in the hard rock region is significantly greater (38%), which cannot be ignored in project budgeting. Ultimately, our results support the use of compressive strength as a classification index for shield tunneling in complex geological conditions, representing a valuable contribution to the field of tunneling cost prediction.

Suggested Citation

  • Xiaomu Ye & Pengfei Ding & Dawei Jin & Chuanyue Zhou & Yi Li & Jin Zhang, 2023. "Intelligent Analysis of Construction Costs of Shield Tunneling in Complex Geological Conditions by Machine Learning Method," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1423-:d:1098135
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/6/1423/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/6/1423/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jörg Kalbfuß & Reto Odermatt & Alois Stutzer, 2018. "Medical marijuana laws and mental health in the United States," CEP Discussion Papers dp1546, Centre for Economic Performance, LSE.
    2. Qingrong Tan & Yan Cai & Fen Luo & Dongbo Tu, 2023. "Development of a High-Accuracy and Effective Online Calibration Method in CD-CAT Based on Gini Index," Journal of Educational and Behavioral Statistics, , vol. 48(1), pages 103-141, February.
    3. Hapfelmeier, A. & Ulm, K., 2013. "A new variable selection approach using Random Forests," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 50-69.
    4. Burim Ramosaj & Markus Pauly, 2019. "Predicting missing values: a comparative study on non-parametric approaches for imputation," Computational Statistics, Springer, vol. 34(4), pages 1741-1764, December.
    5. Limon Barua & Bo Zou & Yan Zhou & Yulin Liu, 2023. "Modeling household online shopping demand in the U.S.: a machine learning approach and comparative investigation between 2009 and 2017," Transportation, Springer, vol. 50(2), pages 437-476, April.
    6. Rachel A. Oldroyd & Michelle A. Morris & Mark Birkin, 2021. "Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach," IJERPH, MDPI, vol. 18(23), pages 1-20, November.
    7. Enrico Biffis & Erik Chavez & Alexis Louaas & Pierre Picard, 2022. "Parametric insurance and technology adoption in developing countries," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 47(1), pages 7-44, March.
    8. Paola Zuccolotto, 2010. "Evaluating the impact of a grouping variable on Job Satisfaction drivers," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(2), pages 287-305, June.
    9. Gerhard Tutz & Moritz Berger, 2016. "Item-focussed Trees for the Identification of Items in Differential Item Functioning," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 727-750, September.
    10. Montes, Ignacio & Miranda, Enrique & Montes, Susana, 2014. "Stochastic dominance with imprecise information," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 868-886.
    11. Shu-Fu Kuo & Yu-Shan Shih, 2012. "Variable selection for functional density trees," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1387-1395, December.
    12. Daniel L. Chen & Markus Loecher, 2022. "Mood and the Malleability of Moral Reasoning: The Impact of Irrelevant Factors on Judicial Decisions," Working Papers hal-03864854, HAL.
    13. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.
    14. Achim Zeileis & Torsten Hothorn, 2013. "A toolbox of permutation tests for structural change," Statistical Papers, Springer, vol. 54(4), pages 931-954, November.
    15. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
    16. Ahlrichs, Jakob & Wenninger, Simon & Wiethe, Christian & Häckel, Björn, 2022. "Impact of socio-economic factors on local energetic retrofitting needs - A data analytics approach," Energy Policy, Elsevier, vol. 160(C).
    17. Hapfelmeier, Alexander & Hornung, Roman & Haller, Bernhard, 2023. "Efficient permutation testing of variable importance measures by the example of random forests," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    18. Hapfelmeier, A. & Ulm, K., 2014. "Variable selection by Random Forests using data with missing values," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 129-139.
    19. Charles B. Perkins & J. Christina Wang, 2019. "How Magic a Bullet Is Machine Learning for Credit Analysis? An Exploration with FinTech Lending Data," Working Papers 19-16, Federal Reserve Bank of Boston.
    20. Yadegaridehkordi, Elaheh & Nilashi, Mehrbakhsh & Nizam Bin Md Nasir, Mohd Hairul & Momtazi, Saeedeh & Samad, Sarminah & Supriyanto, Eko & Ghabban, Fahad, 2021. "Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques," Technology in Society, Elsevier, vol. 65(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1423-:d:1098135. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.