Advertisement

An intelligent model based on statistical learning theory for engineering rock mass classification

  • Kaiyun Liu
  • Baoguo Liu
  • Yu Fang
Original Paper
  • 23 Downloads

Abstract

The engineering classification of rock masses is the basis of rock engineering design and construction. We propose and apply a quick basic quality (BQ) classification method based on the standard BQ method of China to classify the quality grade of the rock mass around tunnels along the Ningguo-Huangshan Expressway during the construction period. Moreover, the joint continuity and surface roughness of the controlled key joint are added to the classification indices of the quick BQ method to address shortcomings of the standard BQ classification method. Therefore, an improved BQ classification method for rock mass is proposed. According to the BQ method, different personnel might select different values of correction coefficient that result in divergences in the result of rock mass classification. In order to solve this problem, the Genetic algorithm (GA) and support vector classification (SVC) coupling algorithm is introduced into the field of engineering rock mass classification. GA is used to automatically search for the optimal SVC parameters during the training process of samples. By training the classification samples of rock mass around a tunnel using the improved BQ method during the tunnel construction period, an intelligent SVC classification model is constructed with inputs based on eight classification indices and an output of the BQ quality grade. To verify the reliability and accuracy of the model, the SVC model is used to evaluate the quality grade of the rock mass around tunnel in other cross sections of the tunnels along the Ningguo-Huangshan Expressway. Only one section classification result differed from those of the improved BQ method in a total of 20 sections. In contrast, three section classification results based on the BP neural network (BPNN) model were inconsistent with those of the improved BQ method. Therefore, the proposed SVC model displays a higher rate of correct classification relative to that of the BPNN model. Meanwhile, the use of this SVC model can avoid the divergence among different people on the classification result of rock mass around a tunnel, which provides an effective new method for the rapid classification of rock mass around a tunnel during tunnel construction.

Keywords

Engineering classification of rock masses BQ classification method Quick BQ classification method Improved BQ classification method GA and SVC coupled algorithm Intelligent classification model 

Notes

Acknowledgements

This study was sponsored by the National Natural Science Foundation of China (No. 51378052).

References

  1. Ali EN (2001) Engineering geology assessment of El-Rabweh landslide, south-east of Amman City, Jordan. Bull Eng Geol Env 60(2):109–116Google Scholar
  2. Alimoradi A, Moradzadeh A, Naderi R, Salehi MZ, Etemadi A (2008) Prediction of geological hazardous zones in front of a tunnel face using TSP-203 and artificial neural networks. Tunn Undergr Space Technol 23(6):711–717CrossRefGoogle Scholar
  3. Aydin A (2004) Fuzzy set approaches to classification of rock masses. Eng Geol 74(3):227–245CrossRefGoogle Scholar
  4. Barton NR, & Bandis S C (1990) Review of predictive capabilities pf JRC-JCS model in engineering practice. In Rock Joints, Proc int symp on rock joints, Loen, Norway (eds N. Barton and O. Stephenson) (pp. 603–610)Google Scholar
  5. Barton N, Lien R, Lunde J (1974) Engineering classification of rock masses for the design of tunnel support. J Rock Mech 6(4):189–236CrossRefGoogle Scholar
  6. Bieniawski ZT (1973) Engineering classification of jointed rock masses. Trans S Afr Inst Civil Eng 15(12):335–344Google Scholar
  7. Bieniawski ZT (1976) Rock mass classification in rock engineering. In: Proc. Symp. On Expl. For Rock Eng. Johannesburg, South Africa, Balkema, Rotterdam, 97–106Google Scholar
  8. Bieniawski ZT (1989) Engineering rock mass classification. Wiely, New YorkGoogle Scholar
  9. Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167CrossRefGoogle Scholar
  10. Cai B, Yu Y, Wu X (2001) Relationship among national code, Q system and RMR in rockmass classification and evaluation of deformation parameter. Chin J Rock Mech Eng 20(supp):1679–1681 (in Chinese)Google Scholar
  11. Cawley GC, Talbot NL (2002) Improved sparse least-squares support vector machines. Neurocomputing 48(1):1025–1031CrossRefGoogle Scholar
  12. Cawley GC, Talbot NL (2004) Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. Neural Netw 17(10):1467–1475CrossRefGoogle Scholar
  13. Chen CS, Liu YC (2007) A methodology for evaluation and classification of rock mass quality on tunnel engineering. Tunneling and Underground Space Technology 22(4):377–387CrossRefGoogle Scholar
  14. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University PressGoogle Scholar
  15. Deere DU, Henderson AJ, Patton FD, Cording EJ (1967) Design of Surface and near surface construction in rock. In: Proc. 8th US Symp. Rock Mech., AIME, New York, 237–302Google Scholar
  16. Fukui K, Okubo S (2006) Some attempts for estimating rock strength and rock mass classification from cutting force and investigation of optimum operation of tunnel boring machines. Rock Mech Rock Eng 39(1):25–44CrossRefGoogle Scholar
  17. Gholami R, Rasouli V, Alimoradi A (2013) Improved RMR rock mass classification using artificial intelligence algorithms. Rock Mech Rock Eng 46(5):1199–1209CrossRefGoogle Scholar
  18. Goh AT, Goh SH (2007) Support vector machines: their use in geotechnical engineering as illustrated using seismic liquefaction data. Comput Geotech 34(5):410–421CrossRefGoogle Scholar
  19. Hamidi JK, Shahriar K, Rezai B, Bejari H (2010) Application of fuzzy set theory to rock engineering classification systems: an illustration of the rock mass excavability index. Rock Mech Rock Eng 43(3):335–350CrossRefGoogle Scholar
  20. Hashemi M, Moghaddas S, Ajalloeian R (2010) Application of rock mass characterization for determining the mechanical properties of rock mass: a comparative study. Rock Mech Rock Eng 43(3):305–320CrossRefGoogle Scholar
  21. Hoek E (1994) Strength of rock and rock masses. News J ISRM 2(2):4–16Google Scholar
  22. Hoek E, Marinos PG, Benissi M (1998) Applicability of the geological strength index (GSI) classification for weak and sheared rock masses-the case of the Athens schist formation. Bull Eng Geol Env 57(2):151–160CrossRefGoogle Scholar
  23. Houck CR, Joines JA, Kay MG (1996) Comparison of genetic algorithms, random restart and two-opt switching for solving large location-allocation problems. Comput Oper Res 23(6):587–596CrossRefGoogle Scholar
  24. Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425CrossRefGoogle Scholar
  25. Jalalifar H, Sahebi AA (2014) Prediction of rock mass rating using fuzzy logic and multi-variable RMR regression model. Int J Min Sci Technol 24(2):237–244CrossRefGoogle Scholar
  26. Jalalifar H, Mojedifar S, Sahebi AA et al (2011) Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system. Comput Geotech 38:783–790CrossRefGoogle Scholar
  27. Jalalifar H, Mojedifar S, Sahebi AA (2014) Prediction of rock mass rating using fuzzy logic and multi-variable RMR regression model. Int J Min Sci Technol 24(2):237–244CrossRefGoogle Scholar
  28. Kaiser PK, MacKay C, Gale AD (1986) Evaluation of rock classifications at BC rail tumbler ridge tunnels. Rock Mech Rock Eng 19(4):205–234CrossRefGoogle Scholar
  29. Klose CD, Loew S, Giese R, Borm G (2007) Spatial predictions of geological rock mass properties based on in-situ interpretations of multi-dimensional seismic data. Eng Geol 93(3):99–116CrossRefGoogle Scholar
  30. Kordjazi A, Nejad FP & Jaksa MB (2014) Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data. Computers & Geotechnics 55(1):91–102CrossRefGoogle Scholar
  31. Laufer H (1958) Classification for tunnel construction (in German). Geologie und Bauwesen 24(1):46–51Google Scholar
  32. Lin F, Huang R, Wang S, et al.(2008). Evaluation of in-situ measurement methods for counting volumetric joints of rock mass. J Eng Geol, 16(5): 663-666. (in Chinese)Google Scholar
  33. Liu YC, Chen CS (2007) A new approach for application of rock mass classification on rock slope stability assessment. Eng Geol 89(1):129–143CrossRefGoogle Scholar
  34. Ministry of Transport of the People’s Republic of China (2004) Code for design of road tunnel. (JTGD70). China Communications Press, Beijing (in Chinese)Google Scholar
  35. Ministry of Transport of the People's Republic of China (2009) Technical specification for construction of highway tunnel (JTG F60). China Communications Press, Beijing (in Chinese)Google Scholar
  36. Mohammad M, Mohammad FH, Heydar B et al (2017) Rock bolt supporting factor: rock bolting capability of rock mass. Bull Eng Geol Env 76(1):231–239Google Scholar
  37. National Railway Administration of the People’s Republic of China (2016) Code for Design of Railway Tunnel. (TB 10003). China railway Press. (in Chinese)Google Scholar
  38. Palmström A (1982) The volumetric joint count- a useful and simple measure of the degree of jointing. Proc. int. congr. IAEG, New Delhi. V.221–V.228Google Scholar
  39. Palmström A (1995) RMi: a rock mass classification system for rock engineering purposes. Ph. D. Thesis. The University of Oslo, 400Google Scholar
  40. Palmström A (2000) Recent development in rock support estimates by the RMi. J Rock Mech Tunn Tech 6(1):1–19Google Scholar
  41. Palmström A (2005) Measurements of and correlations between block size and rock quality designation (RQD). Tunneling and Underground Space Technology 20(4):362–377CrossRefGoogle Scholar
  42. Platt JC (2000) Fast training of support vector machines using sequential minimal optimization. Microsoft Res 41–65Google Scholar
  43. Rad HN, Jalali Z, Jalalifar H (2015) Prediction of rock mass rating system based on continuous functions using chaos-ANFIS model. Int J Rock Mech Min Sci 73:1–9CrossRefGoogle Scholar
  44. Samui P (2008) Support vector machine applied to settlement of shallow foundations on cohesionless soils. Comput Geotech 35(3):419–427CrossRefGoogle Scholar
  45. Şen Z, Eissa EA (1991) Volumetric rock quality designation. J Geotech Eng 117(9):1331–1346CrossRefGoogle Scholar
  46. Şen Z, Eissa EA (1992) Rock quality charts for log-normally distributed block sizes. In International journal of rock mechanics and mining sciences & geomechanics abstracts (Vol. 29, No. 1, pp. 1-12). PergamonGoogle Scholar
  47. Terzaghi K (1946) Rock defects and loads on tunnel support. In: Proctor RV, White T (eds) Rock tunneling with steel supports. Commercial Shearing Co., Youngstown, pp 15–99Google Scholar
  48. The National Standards Compilation Group of People′s Republic of China (1995) GB 50218-94 standard for engineering classification of rock masses [S]. China Planning Press, Beijing (in Chinese)Google Scholar
  49. Tinoco J, Correia AG, Cortez P (2014) Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns. Comput Geotech 55:132–140CrossRefGoogle Scholar
  50. Vapnik VN (1995) The nature of statistical learning theory. Springer-Verlag, New YorkCrossRefGoogle Scholar
  51. Vapnik VN (1998) Statistical learning theory. Wiley, New YorkGoogle Scholar
  52. Wickham GE, Tiedman HR, Skinner EH (1972) Support determination based on geologic predictions, In: Proc. Rapid Exc. Tunneling Conf., AIME, New York, 43–64Google Scholar
  53. Wu AQ, Liu FZ (2012) Advancement and application of the standard of engineering classification of rock masses. Chin J Rock Mech Eng 31(8):1513–1523 (in Chinese)Google Scholar
  54. Xu HF, Chen F, Wang B, Hua ZM, Geng HS (2014) Relationship between RMR and BQ for rock mass classification and estimation of its mechanical parameters. Chin J Geotech Eng 36(1):195–198 (in Chinese)Google Scholar
  55. Yan RX, Shen YJ (2015) Correlation of revised BQ system in China and the international rock mass classification systems. Journal of Civil Engineering Research 5(2):33–38Google Scholar
  56. Zhao YF (1995) Principal conversion methods for rock mass classification systems used at home and abroad. Bulletin of the International Association of Engineering Geology 51(1):81–88CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Civil EngineeringBeijing Jiaotong UniversityBeijingChina
  2. 2.Anhui Transportation Holding Group Co. LTDHefeiChina

Personalised recommendations