Skip to main content
SpringerLink
Log in

Predicting tunnel squeezing using a hybrid classifier ensemble with incomplete data

  • Original Paper
  • Published:
Bulletin of Engineering Geology and the Environment Aims and scope Submit manuscript

Abstract

Tunnel squeezing occurs when time-dependent rock creep produces large tunnel convergence. The occurrence of tunnel squeezing may result in buget increase and time waste during tunnel construction. The aim of this study was to propose a robust classifier ensemble to predict squeezing conditions in rock tunnels. Seven individual machine learning classifiers were aggregated using weighted voting methods to establish the classifier ensemble. The weight and hyperparameters of each individual classifier were tuned using the firefly algorithm. The classifier ensemble was trained and tested on a dataset collected from published literature. Missing values in the database were replaced by various imputation methods. The results indicate that the proposed classifier ensemble achieved an accuracy of 96%, higher than that of the traditionally used individual classifiers. This robust ensemble method can be applied to other classification problems in civil engineering.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Alexandre LA, Campilho AC, Kamel M (2001) On combining classifiers using sum and product rules. Pattern Recogn Lett 22:1283–1289

    Article  Google Scholar 

  • Andrews DF (1974) A robust method for multiple linear regression. Technometrics 16:523–531

    Article  Google Scholar 

  • Aydan Ö, Akagi T, Kawamoto T (1993) The squeezing potential of rocks around tunnels; theory and prediction. Rock Mech Rock Eng 26:137–163

    Article  Google Scholar 

  • Barla G (1995) Squeezing rocks in tunnels. ISRM News Journal 2:44–49

    Google Scholar 

  • Barton N, Lien R, Lunde J (1974) Engineering classification of rock masses for the design of tunnel support. Rock Mech 6:189–236

    Article  Google Scholar 

  • Chen J, Shao J (2000) Nearest neighbor imputation for survey data. J Off Stat 16:113

    Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297

    Google Scholar 

  • Dhanabal S, Chandramathi S (2011) A review of various k-nearest neighbor query processing techniques. Int J Comput Appl 31:14–22

    Google Scholar 

  • Dwivedi R, Singh M, Viladkar M, Goel R (2013) Prediction of tunnel deformation in squeezing grounds. Eng Geol 161:55–64

    Article  Google Scholar 

  • Elbaz K, Shen S-L, Zhou A, Yuan D-J, Xu Y-S (2019) Optimization of EPB shield performance with adaptive neuro-fuzzy inference system and genetic algorithm. Appl Sci 9:780

    Article  Google Scholar 

  • Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874. https://doi.org/10.1016/j.patrec.2005.10.010

    Article  Google Scholar 

  • Feng X, Jimenez R (2015) Predicting tunnel squeezing with incomplete data using Bayesian networks. Eng Geol 195:214–224

    Article  Google Scholar 

  • Fritz P (1984) An analytical solution for axisymmetric tunnel problems in elasto-viscoplastic media. Int J Numer Anal Methods Geomech 8:325–342

    Article  Google Scholar 

  • Gandomi AH, Yang X-S, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18:89–98

    Article  Google Scholar 

  • Gao F, Stead D, Kang H (2015) Numerical simulation of squeezing failure in a coal mine roadway due to mining-induced stresses. Rock Mech Rock Eng 48:1635–1645. https://doi.org/10.1007/s00603-014-0653-2

    Article  Google Scholar 

  • Guan Z, Jiang Y, Tanabashi Y, Huang H (2008) A new rheological model and its application in mountain tunnelling. Tunn Undergr Space Technol 23:292–299

    Article  Google Scholar 

  • Hasanpour R, Rostami J, Ünver B (2014) 3D finite difference model for simulation of double shield TBM tunneling in squeezing grounds. Tunn Undergr Space Technol 40:109–126

    Article  Google Scholar 

  • Hecht-Nielsen R (1992) Theory of the backpropagation neural network[M]//Neural networks for perception. Academic Press 65–93

  • Hoek E (2001) Big tunnels in bad rock. J Geotech Geoenviron 127:726–740

    Article  Google Scholar 

  • Hoek E, Guevara R (2009) Overcoming squeezing in the Yacambú-Quibor tunnel, Venezuela. Rock Mech Rock Eng 42:389–418

    Article  Google Scholar 

  • Hoek E, Marinos P Tunnelling in overstressed rock. In: Proceedings of the Regional Symposium of the International Society for Rock Mechanics, EUROCK, 2009. pp 49–60

  • Jimenez R, Recio D (2011) A linear classifier for probabilistic prediction of squeezing conditions in Himalayan tunnels. Eng Geol 121:101–109. https://doi.org/10.1016/j.enggeo.2011.05.006

    Article  Google Scholar 

  • Li N (2017) Predicting underground tunnel hazards using machine learning techniques. PhD thesis, Universidad Politécnica de Madrid, Spain

  • Li L, Hu Q, Wu X, Yu D (2014) Exploration of classification confidence in ensemble learning. Pattern Recogn 47:3120–3131

    Article  Google Scholar 

  • Little RJ, Rubin DB (2002) Statistical analysis with missing data. Wiley, New York

    Book  Google Scholar 

  • Lyu H-M, Shen S-L, Zhou A-N, Zhou W-H (2019a) Flood risk assessment of metro systems in a subsiding environment using the interval FAHP-FCA approach. Sustain Cities Soc 50:101682. https://doi.org/10.1016/j.scs.2019.101682

    Article  Google Scholar 

  • Lyu H-M, Shen S-L, Zhou A, Yang J (2019b) Perspectives for flood risk assessment and management for mega-city metro system. Tunn Undergr Space Technol 84:31–44

    Article  Google Scholar 

  • Malarvizhi M, Thanamani A (2012) K-NN classifier performs better than K-means clustering in missing value imputation. IOSR J Comp Eng (IOSRJCE) 6:12–15

    Article  Google Scholar 

  • McRae GJ, Tilden JW, Seinfeld JH (1982) Global sensitivity analysis—a computational implementation of the Fourier Amplitude Sensitivity Test (FAST). Comput Chem Eng 6:15–25. https://doi.org/10.1016/0098-1354(82)80003-3

    Article  Google Scholar 

  • Peng C-YJ, Lee KL, Ingersoll GM (2002) An introduction to logistic regression analysis and reporting. J Educ Res 96:3–14

    Article  Google Scholar 

  • Phienwej N, Thakur P, Cording E (2007) Time-dependent response of tunnels considering creep effect. Int J Geomech 7:296–306

    Article  Google Scholar 

  • Pianosi F, Sarrazin F, Wagener T (2015) A Matlab toolbox for global sensitivity analysis. Environ Model Softw 70:80–85. https://doi.org/10.1016/j.envsoft.2015.04.009

    Article  Google Scholar 

  • Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106

    Google Scholar 

  • Rish I An empirical study of the naive Bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, 2001. vol 22. pp 41–46

  • Sakurai S (1997) Lessons learned from field measurements in tunnelling. Tunn Undergr Space Technol 12:453–460

    Article  Google Scholar 

  • Shalabi F (2005) FE analysis of time-dependent behavior of tunneling in squeezing ground using two different creep models. Tunn Undergr Space Technol 20:271–279

    Article  Google Scholar 

  • Shang Y, Xue J, Wang S, Yang Z, Yang J (2004) A case history of tunnel boring machine jamming in an inter-layer shear zone at the Yellow River diversion project in China. Eng Geol 71:199–211

    Article  Google Scholar 

  • Shrestha GL, Broch E (2008) Influences of the valley morphology and rock mass strength on tunnel convergence: with a case study of Khimti 1 headrace tunnel in Nepal. Tunn Undergr Space Technol 23:638–650

    Article  Google Scholar 

  • Singh B, Jethwa J, Dube A, Singh B (1992) Correlation between observed support pressure and rock mass quality. Tunn Undergr Space Technol 7:59–74

    Article  Google Scholar 

  • Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45:427–437

    Article  Google Scholar 

  • Sterpi D, Gioda G (2009) Visco-plastic behaviour around advancing tunnels in squeezing rock. Rock Mech Rock Eng 42:319–339

    Article  Google Scholar 

  • Sun J, Zhang J, Gu Y, Huang Y, Sun Y, Ma G (2019a) Prediction of permeability and unconfined compressive strength of pervious concrete using evolved support vector regression. Constr Build Mater 207:440–449

    Article  Google Scholar 

  • Sun Y et al (2019b) Determination of Young’s modulus of jet grouted coalcretes using an intelligent model. Eng Geol 252:43–53

    Article  Google Scholar 

  • Sun Y, Zhang J, Li G, Wang Y, Sun J, Jiang C (2019c) Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes. Int J Numer Anal Methods Geomech 43:801–813

    Article  Google Scholar 

  • Tóth N, Pataki B (2008) Classification confidence weighted majority voting using decision tree classifiers. Int J Intell Comput Cybern 1:169–192

    Article  Google Scholar 

  • Uebersax JS (1982) A generalized kappa coefficient. Educ Psychol Meas 42:181–183

    Article  Google Scholar 

  • Zhang J, Jiang F, Yang J, Bai W, Zhang L (2017) Rockburst mechanism in soft coal seam within deep coal mines. Int J Min Sci Technol 27:551–556

    Article  Google Scholar 

  • Zhang J, Ma G, Huang Y, Aslani F, Nener B (2019a) Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression. Constr Build Mater 210:713–719

    Article  Google Scholar 

  • Zhang N, Shen S-L, Zhou A, Xu Y-S (2019b) Investigation on performance of neural networks using quadratic relative error cost function. IEEE Access 7:106642–106652

    Article  Google Scholar 

  • Zhifa Y, Zhiyin W, Luqing Z, Ruiguang Z, Nianxing X (2001) Back-analysis of viscoelastic displacements in a soft rock road tunnel. Int J Rock Mech Min Sci 38:331–341

    Article  Google Scholar 

Download references

Acknowledgements

Junfei Zhang and Dong Li contribute equally to this paper and should be regarded as co-first authors.

Funding

The first author is supported by the China Scholarship Council (grant number: 201706460008).

Author information

Authors and Affiliations

  1. Department of Civil, Environmental and Mining Engineering, The University of Western Australia, Perth, 6009, Australia

    Junfei Zhang

  2. School of Safety Engineering, North China Institute of Science and Technology, Beijing, 101601, China

    Dong Li

  3. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 10093, China

    Yuhang Wang

Authors
  1. Junfei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuhang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Junfei Zhang or Dong Li.

Electronic supplementary material

ESM 1

(DOCX 32 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Li, D. & Wang, Y. Predicting tunnel squeezing using a hybrid classifier ensemble with incomplete data. Bull Eng Geol Environ 79, 3245–3256 (2020). https://doi.org/10.1007/s10064-020-01747-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10064-020-01747-5

Keywords

Search

Navigation