Geotechnical and Geological Engineering

, Volume 37, Issue 1, pp 475–489 | Cite as

Prediction of Rock Compressive Strength Using Machine Learning Algorithms Based on Spectrum Analysis of Geological Hammer

  • Qiubing Ren
  • Gang Wang
  • Mingchao LiEmail author
  • Shuai Han
Original Paper


The traditional method to estimate rock compressive strength (RCS) in field operation is dependent on hammering rocks and artificial identification. It is too subjective to get high estimation accuracy. For this reason, the new and non-destructive method uses machine learning algorithms to analyze acoustic characteristics of geological hammer to predict RCS accurately. The hammering sound samples were successively preprocessed by signal enhancement algorithm and double-threshold method to reduce noise and acquire valuable intervals of all. We have also performed the time-frequency domain conversion on sound signal through FFT, which obtained two brand new indexes, amplitude attenuation coefficient and high and low frequency ratio, as the input parameters of models. By contrasting the performance of various models based on k-nearest neighbors, naive Bayes, random forest, artificial neural networks (ANN), and support vector machines (SVM), we uncovered that the prediction accuracy of both SVM and ANN was over 95%, superior to others. Thus, SVM and ANN were better for widespread application in geological surveys and construction acceptance to predict RCS accurately. In addition, characteristic mechanism of acoustic spectrum was explained from microstructure, energy dissipation and filter effect, which indicated why there existed strong correlation between acoustic characteristics and RCS. The current rock mass classification standard was supplemented with the above two characteristic indexes for better identification.


Rock compressive strength Geological hammer Spectrum analysis Machine learning algorithms Rock mass classification 



This research was supported by the National Natural Science Foundation for Excellent Young Scientists of China (Grant No. 51622904), the Tianjin Science Foundation for Distinguished Young Scientists of China (Grant No. 17JCJQJC44000) and the National Natural Science Foundation of China (Grant No. 51621092).


  1. Alpaydin E (2014) Introduction to machine learning. MIT Press, CambridgeGoogle Scholar
  2. Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175–185Google Scholar
  3. Aydin A (2008) ISRM suggested method for determination of the Schmidt hammer rebound hardness: revised version. In: Ulusay R (ed) The ISRM suggested methods for rock characterization, testing and monitoring: 2007–2014. Springer, Heidelberg, pp 25–33Google Scholar
  4. Berouti M, Schwartz R, Makhoul J (1979) Enhancement of speech corrupted by acoustic noise. In: Acoustics, speech, and signal processing, IEEE international conference on ICASSP, pp 208–211Google Scholar
  5. Boashash B (2015) Time-frequency signal analysis and processing: a comprehensive reference. Academic Press, New YorkGoogle Scholar
  6. Brencich A, Cassini G, Pera D, Riotto G (2013) Calibration and reliability of the rebound (Schmidt) hammer test. Civil Eng Archit 1(3):66–78Google Scholar
  7. BS5930 (1981) Code of practice for site investigations. British Standards Institution, LondonGoogle Scholar
  8. China MWR (1995) Standard for engineering classification of rock masses (GB50218-94). China Planning Press, BeijingGoogle Scholar
  9. Farid DM, Zhang L, Rahman CM, Hossain MA, Strachan R (2014) Hybrid decision tree and naive Bayes classifiers for multi-class classification tasks. Expert Syst Appl 41(4):1937–1946CrossRefGoogle Scholar
  10. Fattahi H (2017) Applying soft computing methods to predict the uniaxial compressive strength of rocks from Schmidt hammer rebound values. Comput Geosci 21(4):665–681CrossRefGoogle Scholar
  11. Fattahi H, Karimpouli S (2016) Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods. Comput Geosci 20(5):1075–1094CrossRefGoogle Scholar
  12. Hack R, Huisman M (2002) Estimating the intact rock strength of a rock mass by simple means. In: engineering geology for developing countries. In: Proceedings of 9th congress of the International Association for Engineering Geology and the Environment, Durban, pp 16–20Google Scholar
  13. Hencher SR, Richards LR (2015) Assessing the shear strength of rock discontinuities at laboratory and field scales. Rock Mech Rock Eng 48(3):883–905CrossRefGoogle Scholar
  14. Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844CrossRefGoogle Scholar
  15. Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255–260CrossRefGoogle Scholar
  16. Karakus M, Tutmez B (2006) Fuzzy and multiple regression modelling for evaluation of intact rock strength based on point load, Schmidt hammer and sonic velocity. Rock Mech Rock Eng 39(1):45–57CrossRefGoogle Scholar
  17. Karaman K, Kesimal A (2015) A comparative study of Schmidt hammer test methods for estimating the uniaxial compressive strength of rocks. Bull Eng Geol Environ 74(2):507–520CrossRefGoogle Scholar
  18. Khandelwal M, Monjezi M (2013) Prediction of backbreak in open-pit blasting operations using the machine learning method. Rock Mech Rock Eng 46(2):389–396CrossRefGoogle Scholar
  19. Kido KI (2015) Digital Fourier analysis: fundamentals. Undergraduate lecture notes in physics. Springer, New YorkGoogle Scholar
  20. Lee C, Hyun D, Choi E, Go J (2003) Optimizing feature extraction for speech recognition. IEEE Trans Audio Speech Process 11(1):80–87CrossRefGoogle Scholar
  21. Li Q, Zheng J, Tsai A, Zhou Q (2002) Robust endpoint detection and energy normalization for real-time speech and speaker recognition. IEEE Trans Audio Speech Process 10(3):146–157CrossRefGoogle Scholar
  22. Li F, Wang JA, Brigham JC (2014) Inverse calculation of in situ stress in rock mass using the surrogate-model accelerated random search algorithm. Comput Geotech 61:24–32CrossRefGoogle Scholar
  23. Mahdevari S, Shahriar K, Yagiz S, Shirazi MA (2014) A support vector regression model for predicting tunnel boring machine penetration rates. Int J Rock Mech Min Sci 72:214–229CrossRefGoogle Scholar
  24. Marinos P, Hoek E (2001) Estimating the geotechnical properties of heterogeneous rock masses such as flysch. Bull Eng Geol Environ 60(2):85–92CrossRefGoogle Scholar
  25. Michalski RS, Carbonell JG, Mitchell TM (2013) Machine learning: an artificial intelligence approach. Springer, BerlinGoogle Scholar
  26. Mohamad ET, Armaghani DJ, Momeni E, Abad SVANK (2015) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Environ 74(3):745–757CrossRefGoogle Scholar
  27. Momeni E, Armaghani DJ, Hajihassani M, Amin MFM (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63CrossRefGoogle Scholar
  28. Rodriguez-Galiano V, Sanchez-Castillo M, Chica-Olmo M, Chica-Rivas M (2015) Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geol Rev 71:804–818CrossRefGoogle Scholar
  29. Rojas R (2013) Neural networks: a systematic introduction. Springer, BerlinGoogle Scholar
  30. Salazar F, Toledo MA, Oñate E, Morán R (2015) An empirical comparison of machine learning techniques for dam behaviour modelling. Struct Saf 56:9–17CrossRefGoogle Scholar
  31. Thomson DJ (1982) Spectrum estimation and harmonic analysis. Proc IEEE 70(9):1055–1096CrossRefGoogle Scholar
  32. Ulusay R (2014) The ISRM suggested methods for rock characterization, testing and monitoring: 2007–2014. Springer, HeidelbergGoogle Scholar
  33. Valera M, Guo Z, Kelly P, Matz S, Cantu A, Percus AG, Hyman JD, Srinivasan G, Viswanathan HS (2018) Machine learning for graph-based representations of three-dimensional discrete fracture networks. Comput Geosci. Google Scholar
  34. Wang H, Lin H, Cao P (2017) Correlation of UCS rating with Schmidt hammer surface hardness for rock mass classification. Rock Mech Rock Eng 50(1):195–203CrossRefGoogle Scholar
  35. Yaşar E, Erdoğan Y (2004) Estimation of rock physicomechanical properties using hardness methods. Eng Geol 71(3):281–288Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Hydraulic Engineering Simulation and SafetyTianjin UniversityTianjinChina
  2. 2.Chengdu Engineering Corporation Limited, PowerChinaChengduChina

Personalised recommendations