Abstract Machine learning (ML) may provide a new methodology to directly learn from raw
data to develop constitutive models for soils by using pure mathematic skills. It has …

Engineering-scale problems generally can be described by partial differential equations
(PDEs) or ordinary differential equations (ODEs). Analytical, semi-analytical and numerical …

A unified hypoplastic model is formulated by incorporating the anisotropic critical state
theory to describe the fabric effect in sand under both monotonic and cyclic loading …

This study systematically presents the application of machine learning (ML) algorithms for
constructing a constitutive model for soils. A genetic algorithm is integrated with ML …

Parameter identification using Bayesian approach with Markov Chain Monte Carlo (MCMC)
has been verified only for certain conventional simple constitutive models up to now. This …

It is well known that soil is inherently anisotropic and its mechanical behavior is significantly
influenced by its fabric anisotropy. Hypoplasticity is increasingly being accepted in the …

Inherent anisotropy is often observed in natural clays. This paper presents a hypoplastic
constitutive model to describe the inherent anisotropy of normally and overconsolidated …

In this study, a hypoplastic model is developed to describe the mechanical behaviors of
cemented sand under both monotonic and cyclic loading conditions. A state variable is …

Natural soils usually exhibit microstructure effects that significantly influence their
engineering properties; however, existing hypoplastic constitutive models rarely consider …

This paper presents a simple hypoplastic constitutive model for overconsolidated clays. The
model needs five independent parameters and is as simple as the modified Cam Clay …