Evaluating Neural Network Models for Stress-Strain Prediction in Coarse-Grained Soils

Jiyuan He; Zeli Wang

doi:10.47297/taposatWSP2633-456917.20250603

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Evaluating Neural Network Models for Stress-Strain Prediction in Coarse-Grained Soils

Volume 6, Issue 3

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Evaluating Neural Network Models for Stress-Strain Prediction in Coarse-Grained Soils
Evaluating Neural Network Models for Stress-Strain Prediction in Coarse-Grained Soils
1.North China Electric Power University, School of Control and Computer Engineering,Beinong Road, Beijing, China
2.State grid Beijing electric power company Chaoyang power supply company, 41 Qianmen West Street, Beijing, China
Vol. 6, Issue 3, Pages: 91-99(2025)
Published：25 March 2025
DOI：10.47297/taposatWSP2633-456917.20250603
Accepted：

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He Jiyuan,Wang Zeli.Evaluating Neural Network Models for Stress-Strain Prediction in Coarse-Grained Soils[J].Theory and Practice of Science and Technology,2025,06(03):91-99.
DOI：

He Jiyuan,Wang Zeli.Evaluating Neural Network Models for Stress-Strain Prediction in Coarse-Grained Soils[J].Theory and Practice of Science and Technology,2025,06(03):91-99. DOI： 10.47297/taposatWSP2633-456917.20250603.

摘要

Abstract

Coarse-grained soil plays a crucial role in civil

subgrade

and hydraulic engineering

where its deformation characteristics directly impact structural stability. Traditional triaxial compression tests are essential for studying soil mechanics but face limitations in capturing deformation behavior due to constrained experimental data and fixed test conditions. In response

this study proposes a Bayesian neural network (BNN)-based prediction method to analyze the deformation characteristics of coarse-grained soil while quantifying prediction uncertainty. Using triaxial test data

the performance of artificial neural networks (ANNs)

convolutional neural networks (CNNs)

and BNNs is compared through data preprocessing

model development

and training optimization. The results demonstrate that BNN effectively captures soil deformation behavior while providing reliable uncertainty estimation

enhancing prediction robustness. This study offers a novel approach for intelligent prediction of coarse-grained soil deformation and provides theoretical support for optimizing civil engineering design.

关键词

Keywords

references

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