Research on PFC3D meso-parameters prediction model and strength simulation of backfill based on hemical-mechanical coupling

This study develops and validates a meso-parameter prediction model for backfill materials in PFC3D, enabling efficient numerical construction under varying curing ages and mix ratios. A quantitative correlation was established between hydration degree of the backfill and macroscopic mechanical properties, facilitating prediction of performance evolution. Using the parallel bonded contact model, the effective modulus [Formula: see text]<sup>*</sup> was found to exhibit a linear relationship with the macroscopic elastic modulus E, while the stiffness ratio [Formula: see text] <sub>n</sub>/[Formula: see text] <sub>s</sub> demonstrated a strong correlation with Poisson's ratio v. Compressive strength σ was shown to depend on both [Formula: see text] <sub>n</sub>/[Formula: see text] <sub>s</sub> and the bonding parameter σ<sub>n</sub>/σ<sub>s</sub>. Based on these relationships, a coupled chemo-mechanical prediction framework was developed to integrate hydration, mechanical response, and meso-scale input parameters. Validation against uniaxial and biaxial simulations demonstrated good agreement with laboratory tests, with mean errors below 8.5% for strength and 10% for deformation. Simulated failure modes closely reproduced experimental observations, confirming model reliability. Application to real stope conditions further demonstrated its capability to evaluate backfill strength. This work provides a mechanistic foundation for efficient and accurate parameter determination, supporting predictive modeling of cemented backfill systems.

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Publication Details

Journal
Scientific Reports
Published
2025-12-09
DOI
https://doi.org/10.1038/s41598-025-28620-3
Citations
1
Primary Topic
Tailings Management and Properties
Type
article
Field-Weighted Citation Impact
0.95
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Research on PFC3D meso-parameters prediction model and strength simulation of backfill based on hemical-mechanical coupling

Aixiang Wu, Jiao Jiang, Yong Wang, Chen Cao et al.
1 citations
Scientific Reports
Tailings Management and Properties
0.95
article

Research on PFC3D meso-parameters prediction model and strength simulation of backfill based on hemical-mechanical coupling

Aixiang Wu, Jiao Jiang, Yong Wang, Chen Cao, Defeng Wang
article en
1 citations

Abstract

This study develops and validates a meso-parameter prediction model for backfill materials in PFC3D, enabling efficient numerical construction under varying curing ages and mix ratios. A quantitative correlation was established between hydration degree of the backfill and macroscopic mechanical properties, facilitating prediction of performance evolution. Using the parallel bonded contact model, the effective modulus [Formula: see text]<sup>*</sup> was found to exhibit a linear relationship with the macroscopic elastic modulus E, while the stiffness ratio [Formula: see text] <sub>n</sub>/[Formula: see text] <sub>s</sub> demonstrated a strong correlation with Poisson's ratio v. Compressive strength σ was shown to depend on both [Formula: see text] <sub>n</sub>/[Formula: see text] <sub>s</sub> and the bonding parameter σ<sub>n</sub>/σ<sub>s</sub>. Based on these relationships, a coupled chemo-mechanical prediction framework was developed to integrate hydration, mechanical response, and meso-scale input parameters. Validation against uniaxial and biaxial simulations demonstrated good agreement with laboratory tests, with mean errors below 8.5% for strength and 10% for deformation. Simulated failure modes closely reproduced experimental observations, confirming model reliability. Application to real stope conditions further demonstrated its capability to evaluate backfill strength. This work provides a mechanistic foundation for efficient and accurate parameter determination, supporting predictive modeling of cemented backfill systems.

Scientific ReportsVol. 15(1)
China Nonferrous Metals Changsha Investigation Design Institute (CN), Beijing Information Science & Technology University (CN), University of Science and Technology Beijing (CN)
Openalex Percentile: Top 19%
Tailings Management and Properties
0.95
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