Research on the influence of rock micro-parameter in particle flow code (PFC) on the rock fracture morphology

Lingli Zhang; Ruirui Wang; Biao Li; Jian Hou; Nana Liu

doi:10.2478/cee-2026-0114

$Research on the influence of rock micro-parameter in particle flow code (PFC) on the rock fracture morphology Cover$

Research on the influence of rock micro-parameter in particle flow code (PFC) on the rock fracture morphology

Civil and Environmental Engineering

AHEAD OF PRINT

By: Lingli Zhang, Ruirui Wang, Biao Li, Jian Hou and Nana Liu

Open Access

|Jun 2026

Abstract

Particle Flow Code (PFC) has been widely used in simulation of geotechnical engineering. In the simulation of laboratory test of rock and soil, the generated fracture appears different distribution, which is significantly affected by the micro-parameters. The generated fracture morphology is a suitable constraint to suppress the non-uniqueness of the micro-parameters. In this research, 1517 rock mass models with different micro-parameters are established, and the corresponding uniaxial compression test, Brazilian splitting test, and direct shear test are simulated. The simulation results are classified into multiple groups according to the distribution of the generated fractures. The relationship between the generated fracture morphology and the micro-parameters are researched. In addition, a method for evaluating the fracture morphology based on K-nearest neighbors is proposed, and it is verified by 40 rock numerical model with random micro-parameters. The evaluating accuracy of the uniaxial compression test, the Brazilian splitting test, and the direct shear test is 82.5%, 82.5%, and 85%. The proposed method is helpful for evaluating the generated fracture morphology to save the time cost of micro-parameters calibration, and have potential on suppressing the non-uniqueness of the micro-parameters in the calibration process, and establishing accurate numerical rock model.

References

Castro-Filgueira, U., Alejano, L. R., Arzúa, J., & Ivars, D. M. (2017). Sensitivity analysis of the micro-parameters used in a pfc analysis towards the mechanical properties of rocks. Procedia Engineering, 191, 488–495.
Search in Google Scholar Back to article
Cen, D.F., Huang, D., & Ren, F. (2017). Shear deformation and strength of the interphase between the soil-rock mixture and the benched bedrock slope surface. Acta Geotechnica, 12: 391–413. https://doi.org/10.1007/s11440-016-0468-2
Search in Google Scholar Back to article
Chehreghani, S., Noaparast, M., Rezai, B., & Shafaei, S.Z. (2017). Bonded-particle model calibration using response surface methodology. Particuology, 32: 141–152. https://doi.org/10.1016/j.partic.2016.07.012
Search in Google Scholar Back to article
Cheng, H., Shuku, T., Thoeni, K., Tempone, P., Luding, S., & Magnanimo, V. (2019). An iterative Bayesian filtering framework for fast and automated calibration of DEM models. Comput Methods Appl Mech Eng., 350:268–294. https://doi.org/10.1016/j.cma.2019.01.027
Search in Google Scholar Back to article
Cheng, W., & Eyke, H. (2009). Combining Instance-Based Learning and Logistic Regression for Multilabel Classification. Machine Learning, 76(2):211–225. https://doi.org/10.1007/s10994-009-5127-5.
Search in Google Scholar Back to article
Chi, X., Xie, Y., Chen, D., Wang, Z., & Zhao, X.D. (2022) Study on mesoscopic parameter calibration of layered ore rock based on particle flow. Min Res Dev., 42(10):113–118. https://doi.org/10.13827/j.cnki.kyyk.2022.10.010
Search in Google Scholar Back to article
De, Simone, M., Souza, L.M.S., & Roehl, D. (2019). Estimating DEM microparameters for uniaxial compression simulation with genetic programming. Int J Rock Mech Min Sci, 118:33–41. https://doi.org/10.1016/j.ijrmms.2019.03.024
Search in Google Scholar Back to article
Deng, S.X., Zheng, Y.L., Feng, L.P., Zhu, P.Y., & Ni, Y. (2019). Application of design of experiments in microscopic parameter calibration for hard rocks of PFC3D model. Chinese Journal of Geotechnical Engineering, 41(04): 655–664.
Search in Google Scholar Back to article
Dong, J., & Li, H. (2022). Study on macro-mesoscopic corresponding relationship and parameter calibration method of loess particle flow. Water Resour Hydropower Eng, 53(04):180–191. https://doi.org/10.13928/j.cnki.wrahe.2022.04.017
Search in Google Scholar Back to article
Dong, X. (2022). Study on the influence mechanism of parallel bond model meso parameters on macro parameters and destruction mechanisation. (Master). Hebei University of Science and Technology, Hebei, China. https://doi.org/10.27107/d.cnki.ghbku.2022.000715
Search in Google Scholar Back to article
Francis, A., & Thomas, A. (2023). Sustainability Assessment and Benchmarking Framework for Buildings Using a System Dynamics Modeling and Simulation Approach. Journal of Computing in Civil Engineering, 37(3): 04023005.1–16
Search in Google Scholar Back to article
Hao, B., Zhang, C., Wang, C., & Ren, C. (2022). Study on determination micro-parameters of rock PFC2D model. Coal Sci Technol, 50(04):132–141. https://doi.org/10.13199/j.cnki.cst.2019-0906
Search in Google Scholar Back to article
He, S. (2018). Calibration method and sensitivity analysis of micromechanic parameters for limestone. (Master). Shandong University
Search in Google Scholar Back to article
Hu, L.B., Wang, F.Z., Jiang, F., & Huang, G. (2023). Study on the influence of mineral composition on the mechanical properties of granite based on FDEM-GBM method. Simulation Modelling Practice and Theory, 129: 102834. https://doi.org/10.1016/j.simpat.2023.102834
Search in Google Scholar Back to article
Huang, D., Cen, D.F., Ma, G.W., & Huang, R.Q. (2015). Step-path failure of rock slopes with intermittent joints. Landslides, 12: 911–926 https://doi.org/10.1007/s10346-014-0517-6.
Search in Google Scholar Back to article
Ibarra, J., Estay, D., Pacheco, A., Guzmán, L., Barraza, R., Chacana, F., & Hernández, C. (2022). Bond calibration method for macroparameters using the discrete element method framework, Engineering Fracture Mechanics, 262: 108223. https://doi.org/10.1016/j.engfracmech.2021.108223
Search in Google Scholar Back to article
Ji, S., & Karlovšek, J. (2022). Calibration and uniqueness analysis of microparameters for DEM cohesive granular material. Int J Min Sci Technol, 32(1):121–136.
Search in Google Scholar Back to article
Kocev, D., Džeroski, S., White, M.D., Newell, G.R., & Griffioen, P. (2009) Using single-and multi-target regression trees and ensembles to model a compound index of vegetation condition. Ecological Modelling, 220(8): 1159–1168. https://doi.org/doi:10.1016/j.ecolmodel.2009.01
Search in Google Scholar Back to article
Li, B., Hu, M.M., Zhang, B., Li, N., Shao, W., Nie, L.C., Cao, W., & Xu, B. (2022a) Numerical simulation and experimental studies of rock-breaking methods for pre-grooving-assisted disc cutter. Bulletin of Engineering Geology and the Environment, 81: 90–104 https://doi.org/10.1007/s10064-022-02594-2
Search in Google Scholar Back to article
Li, B. Zhang, B. Hu, M.M. & Liu, B. (2022b). Full-scale linear cutting tests to study the influence of pre-groove depth on rock-cutting performance by TBM disc cutter. Tunnelling and Underground Space Technology, 122: 104366 https://doi.org/10.1016/j.tust.2022.104366
Search in Google Scholar Back to article
Li, X., Huang, M., Wang, G., Xu, K., a Liu, T. (2021) A calibration method for micro parameters based on neural network and flat-joint contact model. Mech Eng 43(03):393–405
Search in Google Scholar Back to article
Li, Z., Liu, G., Yan, C., Dong. K., & Cui, J. (2023) Study on the construction method of particle flow model of rock with primary hidden micro-fissures and the calibration method of micro-parameters. J Eng Geol 31(06):1842–1853. https://doi.org/10.13544/j.cnki.jeg.2021-0594
Search in Google Scholar Back to article
Ma, C. (2020). Intelligent inverse analysis of macro- and micro-parameters of rockfill based on discrete element method and it’s engineering applications. (Doctor). Xi’an University of Technology.
Search in Google Scholar Back to article
Meng, Q.T., Wang, H., Bai, L., Zhao, F., & Bai, J. (2025). "Rock mass properties prediction method in TBM tunnel based on principal component analysis (PCA). Civil and Environmental Engineering, ahead of print, https://doi.org/10.2478/cee-2026-0051.
Search in Google Scholar Back to article
Ni, Y.D., Wang, R.R., Leng, X.L., Xia, F.M., & Wang, F. (2025a). Modelling of PFC geotechnical material parameter relationships based on orthogonal design and BP neural network. Computational Particle Mechanics, 12: 371–398. https://doi.org/10.1007/s40571-024-00806-y
Search in Google Scholar Back to article
Ni, Y.D., Leng, X.L., Wang, R.R., Xia, F.M., Wang, F., & Wang, C.T. (2025b). A DEM parameter calibration method based on BP neural network and genetic algorithm. International Journal for Numerical and Analytical Methods in Geomechanics, 49: 16–48. https://doi.org/10.1002/nag.70043
Search in Google Scholar Back to article
Pan, C., Li, X., He, L., & Li, J.C. (2021). Study on the effect of micro-geometric heterogeneity on mechanical properties of brittle rock using a grain-based discrete element method coupling with the cohesive zone model. International Journal of Rock Mechanics and Mining Sciences, 140: 104680. https://doi.org/10.1016/j.ijrmms.2021.104680
Search in Google Scholar Back to article
Potyondy, D.O., & Cundall, P.A. (2004). A bonded-particle model for rock. International Journal of Rock Mechanics and Mining Sciences, 41: 1329–1364.
Search in Google Scholar Back to article
Qu, T.M., Feng, Y.T., Zhao, T., & Wang, M. (2020). A hybrid calibration approach to Hertz-type contact parameters for discrete element models. Int J Numer Anal Meth Geomech, 44(9):1281–1300.
Search in Google Scholar Back to article
Qu, T.M., Feng, Y.T., Zhao, T., & Wang, M. (2019). Calibration of linear contact stiffnesses in discrete element models using a hybrid analytical-computational framework. Powder Technol, 356:795–807.
Search in Google Scholar Back to article
Shentu, J., & Lin, B. (2023). A novel machine learning framework for efficient calibration of complex DEM model: a case study of a conglomerate sample. Eng Fract Mech, 279:109044. https://doi.org/10.1016/j.engfracmech.2023.109044
Search in Google Scholar Back to article
Su, W.S., Lin, Z., Yang, H.Y., Dang, S.Y., & Wu, H. (2026). Mechanical Behaviour and Evolution of Rock Loosening Zone in in-situ Reconstruction and Expansion Construction of Highway Tunnels. Civil and Environmental Engineering, ahead of print, https://doi.org/10.2478/cee-2026-0077
Search in Google Scholar Back to article
Tong, X., Zhu, X.H., Ma, P.H., & Leng, Y.Q. (2019). Study on the Strength Parameters of Loess in Granular Discrete Element Method. Chinese Journal of Underground Space and Engineering, 15(02): 435–442.
Search in Google Scholar Back to article
Wang, J. (2020). Study on meso-parameter calibration of chlorite schist particle flow based on discrete element method. (Master). Anhui University of Science and Technology. https://doi.org/10.26918/d.cnki.ghngc.2020.000382.
Search in Google Scholar Back to article
Wang, J.W., Chi, S.C., Shao, X.Q., & Zhao, F.X. (2020a). Application of orthogonal-contour method in calibration of microscopic parameters of rockfill materials. Chinese Journal of Geotechnical Engineering, 42(10): 1867–1875.
Search in Google Scholar Back to article
Wang, J., Chi, S., Shao, X., & Zhao, F. (2020b), Application of orthogonal contour method in calibration of microscopic parameters of rockfill materials. Chin J Geotech Eng, 42(10):1867–1875
Search in Google Scholar Back to article
Wang, R.R., & Zhang, L.L. (2023), K-means-based heterogeneous tunneling data analysis method for evaluating rock mass parameters along a TBM tunnel. Scientific Reports, 13(1): 21564.1–16. https://doi.org/10.1038/s41598-023-49033-0.
Search in Google Scholar Back to article
Wang, X.T., Li, S.C., Xu, Z.H., Lin, P., Hu, J., & Wang, W.T. (2019), Analysis of Factors influencing floor water inrush in coal mines: A nonlinear fuzzy interval assessment method. Mine Water Environ, 38 (1), 81–92
Search in Google Scholar Back to article
Wu, L.Z., & AbouRizk, S. (2020). Numerical-Based Approach for Updating Simulation Input in Real Time. Journal of Computing in Civil Engineering, 35(2): 04020067.1–13
Search in Google Scholar Back to article
Wu, S.Y., & Huang, Y.H. (2023) Macro and meso crack evolution of granite specimens with non-straight fissures: a comparison between two bond models. Theoret Appl Fract Mech, 125:103890. https://doi.org/10.1016/j.tafmec.2023.103890
Search in Google Scholar Back to article
Xia, L., & Zeng, Y.W. (2018), Parametric study of smooth joint parameters on the mechanical behavior of transversely isotropic rocks and research on calibration method. Computers and Geotechnics, 98: 1–7. https://doi.org/10.1016/j.compgeo.2018.01.012
Search in Google Scholar Back to article
Xiang, Y.Z., Liu, H.L., Zhang, W.G., Chun, J., Zhou, D., & Xiao, Y. (2018) Application of transparent soil model test and DEM simulation in study of tunnel failure mechanism. Tunnelling and Underground Space Technology, 74: 178–184. https://doi.org/10.1016/j.tust.2018.01.020
Search in Google Scholar Back to article
Xu, Z.H., Wang, Z.Y., Wang, W.Y., Lin, & P., Wu, J. (2022). An integrated parameter calibration method and sensitivity analysis of microparameters on mechanical behavior of transversely isotropic rocks. Comput Geotech, 142:104573. https://doi.org/10.1016/j.compgeo.2021.104573
Search in Google Scholar Back to article
Yoon, J. (2007). Application of experimental design and optimization to PFC model calibration in uniaxial compression simulation. International Journal of Rock Mechanics and Mining Sciences, 44: 871–889. https://doi.org/10.1016/j.ijrmms.2007.01.004
Search in Google Scholar Back to article
Zhong, W., Ding, H., & Fan, L. (2023). Research on mesoscopic parameters calibration of geopolymer concrete upon BP neural network. Eng Mech, 41(7):1–10
Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.2478/cee-2026-0114 | Journal eISSN: 2199-6512 (formerly 1336-5835) | Journal ISSN: 1336-5835

Journal RSS Feed

Language: English

Submitted on: Jan 27, 2026

Accepted on: Apr 24, 2026

Published on: Jun 24, 2026

Published by: University of Žilina

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Numerical simulation,

Particle flow code,

Fractures,

Laboratory test

Related subjects:

Business and economics,

Business management,

Industries,

Environmental management

© 2026 Lingli Zhang, Ruirui Wang, Biao Li, Jian Hou, Nana Liu, published by University of Žilina
This work is licensed under the Creative Commons Attribution 4.0 License.

AHEAD OF PRINT