Deep Convolutional Neural Network-Based Method for Strength Parameter Prediction of Jointed Rock Mass Using Drilling Logging Data
Publication: International Journal of Geomechanics
Volume 21, Issue 7
Abstract
Field evaluation of the strength properties of jointed rock masses is a challenging task in geotechnical engineering. Typically, laboratory tests using small jointed specimens have difficulty determining the strength parameters of jointed rock masses due to the scale dependence of discontinuities and because the tests are expensive and time-consuming. Fast and continuous estimation of the unconfined compressive strength σcm of a jointed rock mass directly using drilling via a deep convolutional neural network (CNN) is a novel and practical field investigation method. This paper presents a CNN framework that includes (1) obtaining a training dataset; (2) determining the unconfined compressive strength σcm via a rock mass quality rating (RMQR) system; (3) training the CNN model; and (4) validating the results using tunnel engineering calculations. A comparison of the CNN predictive results with the true values suggests that the CNN makes good predictions across a wide range of unconfined compressive strengths σc of intact rock, especially for high RQD values. Due to the joint orientation, the unconfined compressive strength σcm of a jointed rock mass cannot be reliably determined using the σcm/σc ∼ RQD relation. By incorporating the physical variables of RQD and σc, which are known to affect the unconfined compressive strength σcm of a jointed rock mass, into the CNN, the proposed CNN model can provide better predictions than the regular CNN model. All the results predicted by the physics-informed CNN are within the accepted error range of 10%. This method is applied to the excavation of the Huangshan Tunnel in the Hanjiang-to-Weihe River Project of China and is verified as reliable via comparative studies with previous works. Thus, the proposed method represents fast and efficient prediction of the strength of jointed rock masses in rock engineering.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This study is sponsored by the National Natural Science Foundation of China (Grant Nos. 11902249 and 11872301), Natural Science Foundation of Shaanxi Province (Shaanxi Province Natural Science Foundation) (Grant No. 2019JQ395), and Education Bureau of Shaanxi Province | Scientific Research Plan Projects of Shaanxi Education Department in China (Grant No. 20JS093). The financial support provided by this sponsor is greatly appreciated.
References
Allègre, C. J., J. L. L. Mouel, and A. Provost. 1982. “Scaling rules in rock fracture and possible implications for earthquake prediction.” Nature 297 (6): 47–49. https://doi.org/10.1038/297047a0.
Aydan, Ö, and S. Dalgic. 1998. “Prediction of deformation behavior of 3-lanes Bolu tunnels through squeezing rocks of North Anatolian Fault Zone (NAFZ).” In Proc., Regional Symp. on Sedimentary Rock Engineering, 228–233. Taipei, Taiwan: Public Construction Commission.
Aydan, Ö, R. Ulusay, and N. Tokashiki. 2014. “A new rock mass quality rating system: Rock mass quality rating (RMQR) and its application to the estimation of geomechanical characteristics of rock masses.” Rock Mech. Rock Eng. 47 (4): 1255–1276. https://doi.org/10.1007/s00603-013-0462-z.
Barr, M. V. 1984. “Instrumented horizontal drilling for tunneling site investigations.” Ph.D. thesis, Faculty of Engineering, Imperial College of Science and Technology.
Beckman, G. H., D. Polyzois, and Y. J. Cha. 2019. “Deep learning-based automatic volumetric damage quantification using depth camera.” Autom. Constr. 99: 114–124. https://doi.org/10.1016/j.autcon.2018.12.006.
Bieniawski, Z. T. 1978. “Determining rock mass deformability: Experience from case histories.” Int. J. Rock Mech. Min. Sci. Geomech. Abstr. 15 (5): 237–247. https://doi.org/10.1016/0148-9062(78)90956-7.
Bishop, C. M. 2006. Pattern recognition and machine learning. Berlin: Springer.
Bouvrie, J. 2006. “Notes on convolutional neural networks.” In Pract. 47–60.
Brady, B. H. G., and E. T. Brown. 1985. Rock mechanics for underground mining. London: George Allen and Unwin.
Brown, E. T. 1993. “The nature and fundamentals of rock engineering.” In Vol. 1 of Compressive rock engineering-principle, practice and projects, edited by J. A. Hudson, 1–23. Oxford, UK: Pergamon Press.
Cai, M., P. K. Kaiser, Y. Tasaka, and M. Minami. 2007. “Determination of residual strength parameters of jointed rock masses using the GSI system.” Int. J. Rock Mech. Min. Sci. 44 (2): 247–265. https://doi.org/10.1016/j.ijrmms.2006.07.005.
Carleo, G., and M. Troyer. 2017. “Solving the quantum many-body problem with artificial neural networks.” Science 355: 602–606. https://doi.org/10.1126/science.aag2302.
Cha, Y. J., W. Choi, and O. Buyukozturk. 2017. “Deep learning-based crack damage detection using convolutional neural network.” Comput.-Aided Civ. Infrastruct. Eng. 32 (3): 361–378.
Cha, Y. J., W. Choi, G. Suh, S. Mahmoudkhani, and O. Büyüköztürk. 2018. “Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types.” Comput.-Aided Civ. Infrastruct. Eng. 33 (9): 731–747. https://doi.org/10.1111/mice.12334.
Chen, J. Y., T. J. Yang, D. M. Zhang, H. W. Huang, and Y. Tian. 2021. “Deep learning based classification of rock structure of tunnel face.” Geosci. Front. 12: 395–404. https://doi.org/10.1016/j.gsf.2020.04.003.
Collobert, R., J. Weston, and L. Bottou. 2011. “Natural language processing (almost) from scratch.” J. Mach. Learn. Res. 12: 2493–2537.
Coon, R. F., and A. H. Merritt. 1970. “Predicting in situ modulus of deformation using rock quality indexes.” In Determination of the in situ modulus of deformation of rock, ASTM STP477, 154–173. West Conshohocken, PA: ASTM.
Deere, D. U., A. J. Hendron, F. D. Patton, and E. J. Cording. 1967. “Design of surface and near surface construction in rock.” In Proc., 8th U.S. Symp. on Rock Mechanics Failure and Breakage of Rock, 237–302. New York: American Institute of Mining, Metallurgical and Petroleum Engineers.
Edelbro, C., J. Sjöberg, and E. Nordlund. 2007. “A quantitative comparison of strength criteria for hard rock masses.” Tunnelling Underground Space Technol. 22: 57–68. https://doi.org/10.1016/j.tust.2006.02.003.
Ferreira, A., and G. Giraldi. 2017. “Convolutional neural network approaches to granite tiles classification.” Expert Syst. Appl. 84: 1–11. https://doi.org/10.1016/j.eswa.2017.04.053.
Franklin, J. A., E. Broch, and G. Walton. 1971. “Logging the mechanical character of rock.” Trans. Inst. Min. Metall., Sect. A 80: A1–A9.
Fu, M. X., S. Liu, and F. Q. Su. 2018. “An experimental study of the vibration of a drill rod during roof bolt installation.” Int. J. Rock Mech. Min. Sci. 104: 20–26. https://doi.org/10.1016/j.ijrmms.2018.02.004.
Gholami, R., V. Rasouli, and A. Alimoradi. 2013. “Improved RMR rock mass classification using artificial intelligence algorithms.” Rock Mech. Rock Eng. 46: 1199–1209. https://doi.org/10.1007/s00603-012-0338-7.
Gokceoglu, C., H. Sonmez, and A. Kayabasi. 2003. “Predicting the deformation moduli of rock masses.” Int. J. Rock Mech. Min. Sci. 40 (5): 701–710. https://doi.org/10.1016/S1365-1609(03)00062-5.
Gu, J. X., et al. 2018. “Recent advances in convolutional neural networks.” Pattern Recognit. 77: 354–377. https://doi.org/10.1016/j.patcog.2017.10.013.
Guo, H. S., X. T. Feng, S. J. Li, C. X. Yang, and Z. B. Yao. 2017. “Evaluation of the integrity of deep rock masses using results of digital borehole televiewers.” Rock Mech. Rock Eng. 50 (6): 1371–1382. https://doi.org/10.1007/s00603-017-1173-7.
Han, S., H. Li, M. C. Li, and X. C. Luo. 2019. “Measuring rock surface strength based on spectrograms with deep convolutional networks.” Comput. Geosci. 133: 104312. https://doi.org/10.1016/j.cageo.2019.104312.
Hashemi, M., S. Moghaddas, and R. Ajalloeian. 2010. “Application of rock mass characterization for determining the mechanical properties of rock mass: A comparative study.” Rock Mech. Rock Eng. 43: 305–320. https://doi.org/10.1007/s00603-009-0048-y.
He, M., N. Li, Z. Q. Zhang, X. C. Yao, Y. S. Chen, and C. H. Zhu. 2019. “An empirical method for determining the mechanical properties of jointed rock mass using drilling energy.” Int. J. Rock Mech. Min. Sci. 116: 64–74. https://doi.org/10.1016/j.ijrmms.2019.03.010.
He, M., N. Li, J. Zhu, and Y. S. Chen. 2020a. “Advanced prediction for field strength parameters of rock using drilling operational data from impregnated diamond bit.” J. Petrol Sci. Eng. 187: 106847. https://doi.org/10.1016/j.petrol.2019.106847.
He, M. M., N. Li, X. C. Yao, and Y. S. Chen. 2020b. “A new method for prediction of rock quality designation in borehole using energy of rotary drilling.” Rock Mech. Rock Eng. 53 (7): 3383–3394. https://doi.org/10.1007/s00603-020-02091-6.
He, M. M., Z. Q. Zhang, J. Zheng, F. F. Chen, and N. Li. 2020c. “A new perspective on the constant mi of the Hoek–Brown failure criterion and a new model for determining the residual strength of rock.” Rock Mech. Rock Eng. 53 (9): 3953–3967. https://doi.org/10.1007/s00603-020-02164-6.
Hezaveh, Y. D., L. P. Levasseur, and P. J. Marshall. 2017. “Fast automated analysis of strong gravitational lenses with convolutional neural networks.” Nature 548: 555. https://doi.org/10.1038/nature23463.
Hoek, E., and E. T. Brown. 1997. “Practical estimates of rock mass strength.” Int. J. Rock Mech. Min. Sci. 34 (8): 1165–1186. https://doi.org/10.1016/S1365-1609(97)80069-X.
Hoek, E., and M. S. Diederichs. 2006. “Empirical estimation of rock mass modulus.” Int. J. Rock Mech. Min. Sci. 43 (2): 203–215. https://doi.org/10.1016/j.ijrmms.2005.06.005.
Hoek, E., P. K. Kaiser, and W. F. Bawden. 1995. Support of underground excavations in hard rock. Rotterdam, Netherlands: A.A. Balkema.
Huang, L. Q., J. Li, H. Hao, and X. B. Li. 2018. “Micro-seismic event detection and location in underground mines by using convolutional neural networks (CNN) and deep learning.” Tunnelling Underground Space Technol. 81: 265–276. https://doi.org/10.1016/j.tust.2018.07.006.
Hudson, J. A., and S. D. Priest. 1983. “Discontinuity frequency in rock masses.” Int. J. Rock Mech. Min. Sci. Geomech. Abst. 20 (2): 73–89. https://doi.org/10.1016/0148-9062(83)90329-7.
Jiang, A. N., and C. A. Tang. 2008. “Forecasting peak acceleration of blasting vibration of rock mass based on PSO–SVM.” In Proc. 20th Chinese Control and Decision Conference, 2541–2545. Shenyang, China: Northeastern University.
Jiang, R., F. Dai, Y. Liu, and A. Li. 2021a. “Fast marching method for microseismic source location in cavern-containing rockmass: Performance analysis and engineering application.” Engineering. https://doi.org/10.1016/j.eng.2020.10.019.
Jiang, R., F. Dai, Y. Liu, A. Li, and P. Feng. 2021b. “Frequency characteristics of acoustic emissions induced by crack propagation in rock tensile fracture.” Rock Mech. Rock Eng. 54 (4): 2053–2065. https://doi.org/10.1007/s00603-020-02351-5.
Kang, Y., and J. Wang. 2010. “A support-vector-machine-based method for predicting large-deformation in rock mass.” Inst. Rock Soil Mech., Chin. Acad. Sci. 3: 1176–1180.
LeCun, Y., and Y. Bengio. 1998. “Convolutional networks for images, speech, and time series.” Vol. 3361 of The Handbook of Brain Theory and Neural Networks, edited by M. A. Arbib, 255–258. Cambridge, MA: MIT Press.
LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep learning.” Nature 521: 436–444. https://doi.org/10.1038/nature14539.
LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner. 1998. “Gradient-based learning applied to document recognition.” Proc. IEEE 86 (11): 2278–2324. https://doi.org/10.1109/5.726791.
Le Cun, Y., L. D. Jackel, B. Boser, J. S. Denker, H. P. Graf, I. Guyon, D. Henderson, R. E. Howard, and W. Hubbard. 1989. “Handwritten digit recognition: Applications of neural network chips and automatic learning.” IEEE Commun. Mag. 27: 41–46. https://doi.org/10.1109/35.41400.
Lin, Y. Z., Z. H. Nie, and H. W. Ma. 2017. “Structural damage detection with automatic feature-extraction through deep learning.” Comput.-Aided Civ. Infrastruct. Eng. 32 (12): 1025–46. https://doi.org/10.1111/mice.12313.
Mitri, H. S., R. Edrissi, and J. G. Henning. 1995. “Finite-element modeling of cable-bolted stopes in hard-rock underground mines.” SME Trans 298: 1897–1902.
Mohammadi, H., and R. Rahmannejad. 2009. “The estimation of rock mass deformation modulus using regression and artificial neural network analysis.” Arab. J. Sci. Eng. 35 (1A): 67–77.
Nicholson, G. A., and Z. T. Bieniawski. 1990. “A nonlinear deformation modulus based on rock mass classification.” Int. J. Min. Geol. Eng. 8 (3): 181–202. https://doi.org/10.1007/BF01554041.
Niu, W., and T. Li. 2010. “An intelligent rock mass classification method based on support vector machines and the development of website for classification.” In Proc., 1st Int. Conf. Information Technology in Geo-Engineering, 75–83. Amsterdam, Netherlands: IOS Press.
Ocak, I., and S. E. Seker. 2012. “Estimation of elastic modulus of intact rocks by artificial neural network.” Rock Mech. Rock Eng. 45: 1047–1054. https://doi.org/10.1007/s00603-012-0236-z.
Pfister, P. 1985. “Recording drilling parameters in ground engineering.” Geodrilling 1985: 8–13.
Priest, S. D., and J. Hudson. 1976. “Discontinuity spacings in rock.” Int. J. Rock Mech. Min. Sci. Geomech. Abstr. 13 (5): 135–148. https://doi.org/10.1016/0148-9062(76)90818-4.
Rechlin, A. J., S. Luth, and R. Giese. 2011. “Rock mass classification based on seismic measurements using support vector machines.” In Chap. 31 in Harmonising rock engineering and the environment, edited by Q. Qian and Y. Zhou. Boca Raton, FL: CRC.
Renshaw, C. E., and J. C. Park. 1997. “Effect of mechanical interactions on the scaling of fracture length and aperture.” Nature 386 (3): 482–484. https://doi.org/10.1038/386482a0.
Schunnesson, H. 1996. “RQD predictions based on drill performance parameters.” Tunnelling Underground Space Technol. 11 (3): 345–351. https://doi.org/10.1016/0886-7798(96)00024-7.
Scoble, M. J., and J. A. Peck. 1987. “Technique for ground characterization using automated production drill monitoring.” Int. J. Surf. Min. Reclam. Environ. 1: 41–54.
Serafim, J. L., and J. P. Pereira. 1983. “Consideration of the geomechanical classification of Bieniawski.” In Vol. 2 of Proc., Int. Symp. on Engineering Geology and Underground Construction, 3–44. Rotterdam, Netherlands: A.A. Balkema.
Sheorey, P. R. 1997. Empirical rock failure criteria. Rotterdam, Netherlands: A.A. Balkema.
Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov et al. 2014. “Dropout: A simple way to prevent neural networks from overfitting.” J. Mach. Learn Res. 15: 1929–1958.
Sudakov, O., E. Burnaev, and D. Koroteev. 2019. “Driving digital rock towards machine learning: Predicting permeability with gradient boosting and deep neural networks.” Comput. Geosci. 127: 91–98. https://doi.org/10.1016/j.cageo.2019.02.002.
Tian, J. W., C. C. Qi, Y. F. Sun, and Z. M. Yaseen. 2020. “Surrogate permeability modelling of low-permeable rocks using convolutional neural networks.” Comput. Methods Appl. Mech. Eng. 366: 113103. https://doi.org/10.1016/j.cma.2020.113103.
Wenlin, N., L. Tianbin, X. Guobin, and Z. Y. Guang. 2011. “Support vector machine based intelligence rock mass classification method.” J. Eng. Geol. 19 (1): 88–92.
Wilkins, A. H., A. Strange, Y. Duan, and X. Luo. 2020. “Identifying microseismic events in a mining scenario using a convolutional neural network.” Comput. Geosci. 137: 104418. https://doi.org/10.1016/j.cageo.2020.104418.
Wu, J. L., H. Xiao, and E. Paterson. 2018. “Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework.” Phys. Rev. Fluids 3: 074602. https://doi.org/10.1103/PhysRevFluids.3.074602.
Xie, B. J., D. H. Ai, and Y. Yang. 2019. “An automatic pixel-level crack identification method for coals experiencing SHPB impact tests.” J. Geophys. Eng. 16: 297–308. https://doi.org/10.1093/jge/gxz007.
Yi, Y. N., Z. J. Zhang, W. C. Zhang, H. H. Jia, and J. Q. Zhang. 2020. “Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region.” Catena 195: 104851. https://doi.org/10.1016/j.catena.2020.104851.
Yudhbir, W. L., and F. Prinzl. 1983. “An empirical failure criterion for rock masses.” In Proc., 5th Int. Congress on Rock Mechanics. Rotterdam, The Netherlands: A.A. Balkema.
Zhang, A., K. C. P. Wang, B. Li, E. Yang, X. Dai, Y. Peng, Y. Fei, Y. Liu, J. Q. Li, and C. Chen. 2017. “Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network.” Comput.-Aided Civ. Infrastruct. Eng. 32 (10): 805–19. https://doi.org/10.1111/mice.12297.
Zhang, L. Y. 2010. “Estimating the strength of jointed rock masses.” Rock Mech. Rock Eng. 43 (4): 391–402. https://doi.org/10.1007/s00603-009-0065-x.
Zhang, L. Y., and H. H. Einstein. 2004. “Using RQD to estimate the deformation modulus of rock masses.” Int. J. Rock Mech. Min. Sci. 41: 337–341. https://doi.org/10.1016/S1365-1609(03)00100-X.
Zhang, Y. G., J. Tang, Z. He, J. Tan, and C. Li. 2021. “A novel displacement prediction method using gated recurrent unit model with time series analysis in the Erdaohe landslide.” Nat. Hazard. 105 (1): 783–813. https://doi.org/10.1007/s11069-020-04337-6.
Zhang, Y. G., J. Tang, R. Liao, M. F. Zhang, Y. Zhang, X. Wang, and Z. Su. 2020. “Application of an enhanced BP neural network model with water cycle algorithm on landslide prediction.” Stochastic Environ. Res. Risk Assess. https://doi.org/10.1007/s00477-020-01920-y.
Zhang, Y. G., and L. Yang. 2020. “A novel dynamic predictive method of water inrush from coal floor based on gated recurrent unit model.” Nat. Hazard. 105 (2): 2027–2043. https://doi.org/10.1007/s11069-020-04388-9.
Zheng, J., X. Wang, Q. Lü, J. Liu, J. Guo, T. Liu, and J. Deng. 2020. “A contribution to relationship between volumetric joint count (Jv) and rock quality designation (RQD) in three-dimensional (3-D) space.” Rock Mech. Rock Eng. 53: 1485–1494. https://doi.org/10.1007/s00603-019-01986-3.
Zheng, J., X. J. Yang, Q. Lü, Y. Zhao, J. Deng, and Z. Ding. 2018. “A new perspective for the directivity of rock quality designation (RQD) and an anisotropy index of jointing degree for rock masses.” Eng. Geol. 240: 81–94. https://doi.org/10.1016/j.enggeo.2018.04.013.
Zhou, X. P., X. C. Huang, and B. Filippo. 2018a. “A three-dimensional long-term strength criterion of rocks based on micromechanical method.” Theor. Appl. Fract. Mech. 97: 409–418. https://doi.org/10.1016/j.tafmec.2017.07.003.
Zhou, X. P., X. C. Huang, and J. X. Li. 2018b. “Reliability assessment of tunnel based on P-wave seismic velocity.” Int. J. Geomech. 18 (11): 06018030. https://doi.org/10.1061/(ASCE)GM.1943-5622.0001245.
Zhou, X. P., X. C. Huang, P. F. Liu, and T. F. Li. 2018c. “A probabilistic method to analyze collapse failure of shallow rectangular tunnels.” Tunnelling Underground Space Technol. 82: 9–19. https://doi.org/10.1016/j.tust.2018.07.029.
Information & Authors
Information
Published In
International Journal of Geomechanics
Volume 21 • Issue 7 • July 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jun 19, 2020
Accepted: Feb 25, 2021
Published online: Apr 28, 2021
Published in print: Jul 1, 2021
Discussion open until: Sep 28, 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Jiasheng Fu, Wei Liu, Xiangyu Zheng, Xiaosong Han, Transfer Forest: A Deep Forest Model Based on Transfer Learning for Early Drilling Kick Detection, Energies, 10.3390/en16052100, 16, 5, (2100), (2023).
- Zhengyuan Feng, Xiaoliang Hu, Zengguo Tian, Baozhu Jiang, Hongshuai Zhang, Wanli Zhang, Bi-LSTM-Based Dynamic Prediction Model for Pulling Speed of Czochralski Single-Crystal Furnace, Journal of Computing and Information Science in Engineering, 10.1115/1.4056138, 23, 4, (2023).
- Weijie Ding, Shijun Hou, Shuaikang Tian, Shufeng Liang, Dianshu Liu, A Bayesian Optimized Variational Mode Decomposition-Based Denoising Method for Measurement While Drilling Signal of Down-the-Hole Drilling, IEEE Transactions on Instrumentation and Measurement, 10.1109/TIM.2023.3264044, 72, (1-14), (2023).
- Haoteng Wang, Mingming He, Jianbin Zhao, Yonghao Zhang, Beibei Yang, Cutting energy characteristics for brittleness evaluation of rock using digital drilling method, Engineering Geology, 10.1016/j.enggeo.2023.107099, 319, (107099), (2023).
- Guofeng Li, Mingming He, Yue Bai, The influence of the heterogeneity of rock mass for surrounding rock stability evaluation of a tunnel, Bulletin of Engineering Geology and the Environment, 10.1007/s10064-023-03145-z, 82, 4, (2023).
- Krzysztof Fuławka, Lech Stolecki, Marcin Szumny, Witold Pytel, Izabela Jaśkiewicz-Proć, Michel Jakić, Michael Nöger, Philipp Hartlieb, Roof Fall Hazard Monitoring and Evaluation—State-of-the-Art Review, Energies, 10.3390/en15218312, 15, 21, (8312), (2022).
- Min Yang, Naifei Liu, Ning Li, Chongbang Xu, Guofeng Li, Mingming Cao, Failure Characteristics and Treatment Measures of Tunnels in Expansive Rock Stratum, Frontiers in Earth Science, 10.3389/feart.2021.805378, 9, (2022).
- M. M. He, K. Q. Wu, Z. Q. Zhang, Introducing plastic zone and tangential stress into tunnelling strain rockburst prediction, Géotechnique Letters, 10.1680/jgele.21.00129, 12, 3, (194-202), (2022).
- Yuanyuan Gao, Na Liu, Peng Liu, Chengnuo Wang, Prediction of stamping parameters for imitation π-shaped lithium battery shells by building variable weight and threshold pelican-BP neural networks, Advances in Mechanical Engineering, 10.1177/16878132221112203, 14, 9, (168781322211122), (2022).
- Wanfei Gao, A New Method for Predicting Residual Strength of Rock in Water Diversion Tunnel Using Drilling Process Monitoring, Shock and Vibration, 10.1155/2022/8054163, 2022, (1-10), (2022).
- See more