Abstract
The rock quality designation (RQD) is a commonly used index for the classification and evaluation of rock mass quality and is widely adopted in mining and geological engineering. The traditional method of obtaining RQD still requires manual measurement of core length and calculation of RQD, which is inefficient. To address this problem, we propose a framework for drilling core image segmentation and RQD estimation from digital images of cores in core boxes based on the deep learning algorithm. The proposed framework is generated by combining the Mask Region-Based Convolutional Neural Networks (Mask R-CNN) instance segmentation algorithm, the U-Shaped Convolutional Neural Networks (U-Net) semantic segmentation algorithm, and the image processing functions of the Open Source Computer Vision Library (OpenCV). To demonstrate the accuracy of the proposed method, seven boreholes in the Xiushuihe vanadium-titanium magnetite mine, located in the Sichuan Province, China, were used as the case study. According to the comparison of the manual measurements and calculations of the cores taken from seven boreholes used to conduct this study, the framework can record the length of the drill cores and calculate RQD within an average error rate of 3.42%, while it saves about 85% of the working time. The results illustrate that the proposed framework enhances the efficiency of RQD calculation while satisfying the engineering requirements.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
The code for this study was written in Python and is available for download at https://github.com/rui143/A-new-framework/releases/tag/V1.0. The dataset acquired by cellphone in this study is available from the corresponding author upon reasonable request.
References
Deere DU, Hendron AJ, Patton FD et al (1967) Design of surface and near-surface construction in rock. Failure and breakage of rock. In: Processing of the 8th U.S. Symposium on Rock Mechanics, pp 237–302
Deere DU (1989) Rock quality designation (RQD) after 20 years. In: US Army Corps Engrs Contract Report GL-89-1. Waterways Experimental Station, Vicksburg, MS
Laubscher DH (1977) Geomechanics classification of jointed rock masses: mining applications. Inst. Min. Metall., Trans., Sect. A;(United Kingdom) 86
Lucian C, Wangwe EM (2013) The usefulness of rock quality designation (RQD) in determining strength of the rock. Int Refereed J Eng Sci 2(9):36–40
Coon RF, Merritt AH (1970) Predicting in situ modulus of deformation using rock quality indexes. In: Determination of the in situ modulus of deformation of rock. ASTM International, pp 154–173
Gardner WS (1987) Design of drilled piers in the Atlantic Piedmont. In: Smith RW (ed) Foundations and excavations in decomposed rock of the Piedmont province. American Society of Civil Engineers (ASCE), New York, UK, pp 62–86
Zhang LY, Einstein HH (2004) Using RQD to estimate the deformation modulus of rock masses. Int J Rock Mech Min Sci 41(2):337–341
Kulhawy FH, Goodman RE (1987) Foundations in rock. In: Bell FG (ed) Ground Engineer’s reference book, chapter 15. Butterworths, London, UK
Zhang LY (2016) Determination and applications of rock quality designation (RQD). J Rock Mech Geotech Eng 8(3):389–397. https://doi.org/10.1016/j.jrmge.2015.11.008
Hoek E, Carter TG, Diederichs MS (2013) Quantification of the geological strength index chart. In: 47th US Rock Mechanics/Geomechanics Symposium, San Francisco, California, pp 1757–1764
Qureshi MU (2013) Using RQD to estimate the in-situ permeability of discontinuous sedimentary rock. In: Proceedings of the 5th International Young Geotechnical Engineers' Conference. IOS Press, Amsterdam, Netherlands, pp 447–450. https://doi.org/10.3233/978-1-61499-297-4-447
Mathews KE, Hoek E et al (1980) Prediction of stable excavation spans for mining at depths below 1,000 meters in hard rock. In: Golder Associates Report to Canada Centre for Mining and Energy Technology (CAANMET), Department of Energy and Resources, Ottawa, Canada
Bieniawski ZT (1973) Engineering classification of jointed rock masses. Civ Eng Sivil Ing 12:335–343
Barton N, Lien R, Lunde J (1974) Engineering classification of rock masses for the design of tunnel support. Rock Mech 6:189–236
Laubscher DH (1990) A geomechanics classification system for the rating of rock mass in mine design. J South Afr Inst Min Metall 90:257–273. https://doi.org/10.10520/AJA0038223X_1954
Chen JP, Wang Q, Zhao HL (2004) Obtaining RQD of rock mass by sampling window method. Chin J Rock Mech Eng 23:1491–1495
Elsayed AE, Zekai S (1991) Fracture simulation and multi-directional rock quality designation. Bull Assoc Eng Geol 28:193–201. https://doi.org/10.2113/gseegeosci.xxviii.2.193
Zhang W, Chen JP, Cao Z et al (2013) Size effect of RQD and generalized representative volume elements: a case study on an underground excavation in Baihetan dam, Southwest China. Tunn Undergr Space Tech 35:89–98. https://doi.org/10.1016/j.tust.2012.12.007
Zhang W, Chen JP, Niu CC et al (2013) Determination of RQD and number of optimum scanlines based on three-dimensional fracture network. Chin J Geotech Eng 35(2):321–327
Solomon C, Breckon T (2011) Fundamentals of digital image processing: a practical approach with examples in Matlab. John Wiley & Sons, Ltd, pp 1–19
Umbaugh SE (2005) Computer imaging: digital image analysis and processing. CRC press
Mittal M, Verma A, Kaur L et al (2019) An efficient edge detection approach to provide better edge connectivity for image analysis. IEEE Access 7:33240–33255. https://doi.org/10.1109/ACCESS.2019.2902579
Zhang WG, Li H, Li Y et al (2021) Application of deep learning algorithms in geotechnical engineering: a short critical review. Artif Intell Rev 54:5633–5673. https://doi.org/10.1007/s10462-021-09967-1
Karimpouli S, Tahmasebi P (2019) Image-based velocity estimation of rock using Convolutional neural networks. Neural Netw 111:89–97. https://doi.org/10.1016/j.neunet.2018.12.006
Jin CY, Wang K, Han T et al (2022) Segmentation of ore and waste rocks in borehole images using the multi-module densely connected U-net. Comput Geosci 159:105018. https://doi.org/10.1016/j.cageo.2021.105018
Li QB, Du PZ (2020) Automatic RQD analysis method based on information recognition of borehole images. Chin J Geotech Eng 42(11):2153–2160. https://doi.org/10.11779/CJGE202011022
Saricam T, Ozturk H (2018) Estimation of RQD by digital image analysis using a shadow-based method. Int J Rock Mech Min Sci 112:253–265. https://doi.org/10.1016/j.ijrmms.2018.10.032
Liu FY, Liu YH, Yang TH et al (2021) Meticulous evaluation of rock mass quality in mine engineering based on machine learning of core photos. Chin J Geotech Eng 43(05):968–974
He KM, Gkioxari G, Dollár P et al (2017) Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp 2961–2969
Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A (eds) Medical image computing and computer-assisted intervention–MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham. https://doi.org/10.1007/978-3-319-24574-4_28
Malbog MA (2019) MASK R-CNN for Pedestrian Crosswalk Detection and Instance Segmentation. In: 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS). IEEE, Kuala Lumpur, Malaysia, pp 1–5. https://doi.org/10.1109/ICETAS48360.2019.9117217
Chiao JY, Chen KY, Liao KY et al (2019) Detection and classification the breast tumors using mask R-CNN on sonograms. Med 98(19):e15200. https://doi.org/10.1097/MD.0000000000015200
Shi J, Li Z, Zhu T et al (2020) Defect detection of industry wood veneer based on NAS and multi-channel mask R-CNN. Sens 20(16):4398. https://doi.org/10.3390/s20164398
Du G, Cao X, Liang J et al (2020) Medical image segmentation based on U-Net: a review. J Imaging Sci Technol 64:20508. https://doi.org/10.2352/J.ImagingSci.Technol.2020.64.2.020508
Abdollahi A, Biswajeet P et al (2020) Deep learning approaches applied to remote sensing datasets for road extraction: a state-of-the-art review. Remote Sens 12:1444. https://doi.org/10.3390/rs12091444
Jia Y, Shelhamer E, Donahue J et al (2014) Caffe: Convolutional architecture for fast feature embedding. In: MM '14: Proceedings of the 22nd ACM international conference on Multimedia, pp 675–678. https://doi.org/10.1145/2647868.2654889
Acknowledgements
The study was supported by the National Natural Science Foundation of China (Grant 41272333) and the National Program on Key Basic Research Project of China (Grant 2011CB013501). Thanks are due to geological engineer Xi’en Lei and geological engineer Kuan Ye for their assistance with the paper. We gratefully acknowledge the anonymous reviewers and the editors for their constructive suggestions.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of Interest
The authors declare no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Su, R., Zhao, Q., Zheng, T. et al. A Framework for RQD Calculation Based on Deep Learning. Mining, Metallurgy & Exploration 40, 1567–1583 (2023). https://doi.org/10.1007/s42461-023-00805-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42461-023-00805-2