## 1 Introduction
[PERSON] used the flat-jointed contact model as the particle contact constitutive model and conducted six trial-and-error experiments to calibrate the micromechanical parameters for the limestone uniaxial compression model [11]. [PERSON] introduced a calibration procedure for the micromechanical parameters of a discrete element model for greissic granite, with simulation results demonstrating good agreement with the expected outcomes [12]. [PERSON] developed a calibration method for the micromechanical parameters of hollow cylindrical gray sandstone using uniaxial and conventional triaxial compression tests based on an iterative approach [13]. [PERSON] proposed two calibration methods for bond strength parameters of ballast particles in discrete element simulation using the GA-BP neural network [14]. [PERSON] introduced a mesoscopic parameter calibration method for geopolymer concrete based on the analysis of 729 data sets [15]. [PERSON] established fitting relationships between macro- and micromechanical parameters by deriving analytical equations and validating them through extensive numerical simulations [16]. [PERSON] developed empirical correlations between macro- and micromechanical parameters to characterize the triaxial behavior of marble and validated the findings via case studies [17]. Currently, the calibration of micromechanical parameters in discrete element modeling still primarily depends on trial-and-error approaches, which remain inefficient and are hindered by complex, poorly understood parameter interactions. Furthermore, prior studies have primarily focused on geological materials such as high-strength crystalline rocks and unconsolidated sands, while moderately consolidated sandstones remain underexplored in terms of both modeling strategies and systematic parameter calibration. To effectively address the current research gap concerning moderately consolidated sandstone, this study proposes a rapid methodology for constructing discrete element models and calibrating their mechanical parameters. Furthermore, this work advances existing approaches by incorporating multiple sets of micromechanical parameters and conducting orthogonal experimental designs across seven critical variables, thereby enhancing the accuracy and efficiency of the modeling process. Through a multivariate analysis of variance and interaction effect analysis, the key micromechanical parameters influencing macroscopic mechanical responses are systematically identified, revealing their coupling mechanisms and influence patterns. A dynamic regression framework is further employed to construct a multi-factor predictive model, thereby proposing an efficient and data-driven parameter calibration approach. Comparison with laboratory test results demonstrates that the relative errors in simulated macroscopic mechanical parameters remain below 10%, validating the accuracy and robustness of the proposed methodology. This work significantly reduces the empirical dependence inherent in conventional calibration techniques and offers a theoretical foundation for investigating the failure mechanisms and constitutive modeling of moderately consolidated sandstones.