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Prof. Dr. Jianfeng Wang

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Department of Architecture and Civil Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
Interests: micromechanics of granular media; X-ray microtomography; discrete element modeling (DEM); slope stability; multiscale modeling and simulation of geomaterials; computational geomechanics
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Dr. Wei Xiong

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Guest Editor

Department of Architecture and Civil Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
Interests: multiscale particle morphology of granular soils; multiscale modeling and simulation of geomaterials; data-driven constitutive modeling of granular materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This issue delves into both classical and emerging perspectives on the micro- and macro-mechanical behaviors of granular materials. Granular materials are ubiquitous in both nature and technology, ranging from sandcastles built on beaches to indispensable computer chips that underpin modern life. In disciplines such as soil mechanics and powder technology, the study of granular materials boasts a long history, with efforts focused on understanding and predicting their complex responses under various loading conditions. With the rapid advancement of optical instruments, imaging technologies, and numerical simulation methods, researchers now have unprecedented tools to explore the mechanics of granular materials, bringing us closer to a more comprehensive understanding.

We welcome researchers to submit their latest discoveries and insights in the form of full-length articles or reviews. Topics of interest include but are not limited to the development and application of novel experimental techniques, the microstructural and micromorphological characterization of geomaterials, the numerical modeling and analysis of fundamental granular behaviors, constitutive modeling of granular materials, data-driven approaches to constitutive modeling, and machine learning-assisted methods in this domain.

We look forward to receiving your contributions.

You may choose our Joint Special Issue in Materials.

Prof. Dr. Jianfeng Wang
Dr. Wei Xiong
Guest Editors

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23 pages, 4773 KB

Open AccessArticle

Predicting Constitutive Behaviour of Idealized Granular Soils Using Recurrent Neural Networks

by Xintong Li and Jianfeng Wang

Appl. Sci. 2025, 15(17), 9495; https://doi.org/10.3390/app15179495 - 29 Aug 2025

Viewed by 305

Abstract

The constitutive modelling of granular soils has been a long-standing research subject in geotechnical engineering, and machine learning (ML) has recently emerged as a promising tool for achieving this goal. This paper proposes two recurrent neural networks, namely, the Gated Recurrent Unit Neural Network (GRU-NN) and the Long Short-Term Memory Neural Network (LSTM-NN), which utilize input parameters such as the initial void ratio, initial fabric anisotropy, uniformity coefficient, mean particle size, and confining pressure to establish the high-dimensional relationships of granular soils from micro to macro levels subjected to triaxial shearing. The research methodology consists of several steps. Firstly, 200 numerical triaxial tests on idealized granular soils comprising polydisperse spherical particles are performed using the discrete element method (DEM) simulation to generate datasets and to train and test the proposed neural networks. Secondly, LSTM-NN and GRU-NN are constructed and trained, and their prediction performance is evaluated by the mean absolute percentage error (MAPE) and R-square against the DEM-based datasets. The extremely low error values obtained by both LSTM-NN and GRU-NN indicate their outstanding capability in predicting the constitutive behaviour of idealized granular soils. Finally, the trained ML-based models are applied to predict the constitutive behaviour of a miniature glass bead sample subjected to triaxial shearing with in situ micro-CT, as well as to two extrapolated test sets with different initial parameters. The results show that both methods perform well in capturing the mechanical responses of the idealized granular soils. Full article

(This article belongs to the Special Issue Influence of Micro- and Macrostructures on the Behavior and Properties of Geomaterials)

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