A Novel Method of Calibrating Micro-Scale Parameters of PFC Model and Experimental Validation

• Main Authors: Hao Wu, Bing Dai, Guoyan Zhao, Ying Chen, Yakun Tian
• Format: Article
• Language: English
• Published: MDPI AG 2020-05-01
• Series: Applied Sciences
• Subjects: micro-scale parameters; parallel-bond model; particle flow code; response surface method; desirability function; crack development
• Online Access: https://www.mdpi.com/2076-3417/10/9/3221

As a powerful numerical analysis tool, PFC (Particle Flow Code) is widely applied to investigate the mechanical behavior of rock specimen or rock engineering under different stress states. To match the macroscopic properties of the PFC model with those of the rock, a set of micro-scale parameters of the model needs to be calibrated. Thus, this paper proposed an optimization method combining Box–Behnken experimental design and desirability function approach to quickly and accurately find the values of the micro-scale parameters. The sensitivity of the main micro-scale parameters (mean value of parallel-bond normal strength <i>σ_c</i>, ratio of particle normal to shear stiffness <i>E</i>_c, and Young’s modulus at each particle–particle contact <i>k</i>_n/<i>k</i>_s) and their interactions to the macroscopic responses (uniaxial compressive strength, Young’s modulus, and Poisson’s ratio) were thoroughly analyzed using response surface theory. After that, validation study was conducted on the calibrated model. The results manifest that the uniaxial compressive strength is extremely significantly affected by <i>σ_c</i> and <i>k</i>_n/<i>k</i>_s, the Young’s modulus is highly correlated with <i>E</i>_c and <i>k</i>_n/<i>k</i>_s, and the Poisson’s ratio is most significantly influenced by <i>k</i>_n/<i>k</i>_s. Additionally, the interaction of micro-scale parameters also has different impact upon the responses. Moreover, the simulated crack behavior around differently shaped openings in rock samples under uniaxial compression is found to be well agreeable with the experimental results, which verifies the reliability of the proposed method.