A combined neural network/gradient-based approach for the identification of constitutive model parameters using self-boring pressuremeter tests

DOI10.1002/NAG.750zbMath1273.74294OpenAlexW2120996306MaRDI QIDQ2856356

Andrzej Truty, Laurent Vulliet, Rafał F. Obrzud

Publication date: 24 October 2013

Published in: International Journal for Numerical and Analytical Methods in Geomechanics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/nag.750

zbMATH Keywords

neural networksparameter identificationmodified cam clayself-boring pressuremetersoft clay deposit

Mathematics Subject Classification ID

Soil and rock mechanics (74L10) Inverse problems in equilibrium solid mechanics (74G75)

Uses Software

• UCODE

Cites Work

• Optimization framework for calibration of constitutive models enhanced by neural networks
• Soil parameter identification using a genetic algorithm
• Analysis of pressuremeter geometry effects in clay using critical state models
• Computer‐aided calibration of a soil plasticity model
• An analytical solution for the consolidation around a driven pile
• Stress and pore pressure changes in clay during and after the expansion of a cylindrical cavity
• UNDRAINED CAVITY EXPANSIONS IN CRITICAL STATE SOILS

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