[HTML][HTML] Extensive overview of soil constitutive relations and applications for geotechnical engineering problems
A state-of-the-art review has been conducted in this work on soil constitutive modeling,
which has emphasized on: soil type, ground-water conditions, loading conditions, structural …
which has emphasized on: soil type, ground-water conditions, loading conditions, structural …
Review of deep learning-based methods for non-destructive evaluation of agricultural products
Deep Learning (DL) has emerged as a pivotal modelling tool in various domains because of
its proficiency in learning distributed representations. Numerous DL algorithms have …
its proficiency in learning distributed representations. Numerous DL algorithms have …
Physics-informed multifidelity residual neural networks for hydromechanical modeling of granular soils and foundation considering internal erosion
Coupled hydromechanical finite-element modeling of granular soils, taking into account
internal erosion, is computationally prohibitive. Alternative data-driven approaches require …
internal erosion, is computationally prohibitive. Alternative data-driven approaches require …
Physics‐constrained hierarchical data‐driven modelling framework for complex path‐dependent behaviour of soils
There is considerable potential for data‐driven modelling to describe path‐dependent soil
response. However, the complexity of soil behaviour imposes significant challenges on the …
response. However, the complexity of soil behaviour imposes significant challenges on the …
Hybrid random forest-based models for predicting shear strength of structural surfaces based on surface morphology parameters and metaheuristic algorithms
The prediction of shear strength between soil-structure interactions is of great significance to
the stability of geotechnical engineering. In this study, 480 morphological data with seven …
the stability of geotechnical engineering. In this study, 480 morphological data with seven …
A machine learning-based multi-scale computational framework for granular materials
With the development of experimental measurement technology and high-fidelity numerical
simulations of granular materials, empirical-based classical constitutive models may not be …
simulations of granular materials, empirical-based classical constitutive models may not be …
Modeling of frozen soil-structure interface shear behavior by supervised deep learning
This paper proposes a data-driven approach to characterize the interface shear behavior
between frozen soil and structure surface, which can be regularly encountered in …
between frozen soil and structure surface, which can be regularly encountered in …
DEM study on shear behavior of geogrid-soil interfaces subjected to shear in different directions
Y Jia, J Zhang, X Chen, C Miao, Y Zheng - Computers and Geotechnics, 2023 - Elsevier
A three-dimensional discrete element method (DEM) model was developed to explore the
influences of shear direction and geogrid anisotropy on the shear strength behavior of …
influences of shear direction and geogrid anisotropy on the shear strength behavior of …
A predictive deep learning framework for path-dependent mechanical behavior of granular materials
As we transition into an era of data generation and collection, empirical summaries in the
classical continuum modeling of granular materials cannot take full advantage of the …
classical continuum modeling of granular materials cannot take full advantage of the …
A hybrid model enhancing streamflow forecasts in paddy land use-dominated catchments with numerical weather prediction model-based meteorological forcings
The development of a streamflow forecasting tool becomes a challenging task due to the
sophisticated nonlinear catchment response, varying crop management practices, and …
sophisticated nonlinear catchment response, varying crop management practices, and …