Data-Driven Computation Scheme for Duncan–Chang EB Model
This paper extends the data-driven computational mechanics paradigm to nonlinear materials characterized by the Duncan–Chang Elastic-Bulk (E-B) constitutive model. Unlike in linear elastic systems, geotechnical media exhibit stress-dependent tangent moduli and non-convex constitutive manifolds. We propose a recursive nested data-driven solver that dynamically adapts the phase-space distance metric to account for pressure-dependent hardening. A rigorous mathematical analysis of convergence is provided, demonstrating that the solver’s performance is governed by the local transversality between the conservation law constraint set and the nonlinear material manifold. We derive explicit error bounds that couple spatial discretization resolution with material data density. Numerical experiments using triaxial test data from a high-altitude region validate the theoretical predictions, showing that the proposed scheme demonstrates convergence in single-element tests.
Authors
- Hezuo Zhang
- Chaojun Han
- Qianhui Liu
- Xiaohang Li
Institutions
- Hohai University (CN)
- Institute of Disaster Prevention (CN)
- PowerChina (China) (CN)
- Powerchina Huadong Engineering Corporation (China) (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Mathematics
- Published
- 2026-02-24
- DOI
- https://doi.org/10.3390/math14050751
- Primary Topic
- Model Reduction and Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00