An automatic parameterization tool for the hardening soil model

This research introduces a powerful tool, the automatic parametrization of hardening soil (HS) model (APHS), designed to make the HS model parameterization process easier and faster than conventional methods while maintaining high accuracy. Traditional parameterizations rely on oedometer tests, unloading-reloading data, or domain-specific assumptions. Existing optimization-based models often assume uniform parameter weighting, potentially overlooking the distinct sensitivity of each parameter. APHS addresses these limitations as a standalone tool that relies exclusively on conventional triaxial loading test data. To achieve this goal and address the scarcity of labeled datasets, this study integrates numerical modeling with deep learning. The study focuses on a typical shallow Hong Kong soil with parameter ranges derived from field data and relevant literature. Latin hypercube sampling generated diverse parameter values within theoretical bounds for reliable input, while a two-dimensional (2D) axisymmetric finite element model (SIGMA/W) simulated laboratory tests to create a comprehensive, labeled dataset. Seven novel multi-parallel deep long short-term memory (LSTM) networks were trained and validated, achieving an accuracy of 99.4 %. Validation against a conventionally parameterized reference case confirmed 99.6 % accuracy, while an experimental laboratory case study demonstrated strong agreement between simulated and measured results. APHS accelerates HS model parameterization, delivering accurate results in seconds. It can seamlessly integrate with finite element models for automated laboratory data processing and physically informed models to refine calibration parameter ranges. Future work will expand its applicability to various conditions and parameters.