Physics-Informed Active Learning for Calibrating Mesoscopic Dynamic Parameters of Multiphase Concrete in DEM Simulations
Year: 2026
Type: article
Abstract: The discrete element method (DEM) is widely used to simulate concrete failure, but calibrating its mesoscopic dynamic parameters is computationally expensive due to the high-dimensional parameter space. This study proposes a physics-informed active learning framework to autonomously calibrate these parameters under impact loads. An FDM-DEM coupled split Hopkinson pressure bar model is established to simulate macroscopic dynamic compressive responses. Subsequently, a Plackett–Burman experimental design reduces the parameter optimization space from 16 to 8 core dimensions. A multi-layer perceptron surrogate model is then constructed. By comparing two heuristic active sampling strategies, results indicate that a parameter priority-guided strategy incorporating physical priors significantly outperforms a mid-value exploration strategy. The proposed approach achieves coefficients of determination exceeding 0.9 for predicting multiple macroscopic dynamic indicators on an independent testing set. Building upon this forward mapping, a robust inverse parameter prediction mechanism is established, achieving a closed-loop reconstruction of 0.8662. This framework provides a reliable, data-efficient, and automated pathway for calibrating complex multiphase particulate systems.
Source: Buildings
Authors: Jinyuan Huang, Zhongyuan Li, Tingting Zhao
Institution: Taiyuan University of Technology
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G4613140049
OpenAlex ID: W7157250629