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.

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Publication Details

Journal
Mathematics
Published
2026-02-24
DOI
https://doi.org/10.3390/math14050751
Primary Topic
Model Reduction and Neural Networks
Type
article
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Data-Driven Computation Scheme for Duncan–Chang EB Model

Hezuo Zhang, Chaojun Han, Qianhui Liu, Xiaohang Li
Mathematics
Model Reduction and Neural Networks
article

Data-Driven Computation Scheme for Duncan–Chang EB Model

Hezuo Zhang, Chaojun Han, Qianhui Liu, Xiaohang Li
article en

Abstract

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.

MathematicsVol. 14(5)
Hohai University (CN), Institute of Disaster Prevention (CN), PowerChina (China) (CN), Powerchina Huadong Engineering Corporation (China) (CN), Tsinghua University (CN)
Life in Land
Openalex Percentile: Top 42%
Model Reduction and Neural Networks
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