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

6卷第3期

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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
2025年6卷第3期页码：91-99
纸质出版日期：2025-03-25
DOI：10.47297/taposatWSP2633-456917.20250603
Accepted：

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(2025). Evaluating Neural Network Models for Stress-Strain Prediction in Coarse-Grained Soils. 科技理论与实践（英文版）, 6(3), 91-99.

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.
(2025). Evaluating Neural Network Models for Stress-Strain Prediction in Coarse-Grained Soils. 科技理论与实践（英文版）, 6(3), 91-99. DOI： 10.47297/taposatWSP2633-456917.20250603.

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

M. Liu and X. Shi, "Brief Introduction of Engineering Property for Coarse Grained Soil," in IOP Conference Series: Earth and Environmental Science, 2019, vol. 267 , no. 3: IOP Publishing, p. 032047

R. Ventini, A. Flora, S. Lirer, and C. Mancuso, "On the effect of grading and degree of saturation on rockfill volumetric deformation," in Geotechnical Research for Land Protection and Development: Proceedings of CNRIG 2019 7, 2020: Springer, pp. 462-471

C. L. Monismith, N. Ogawa, and C. Freeme, "Permanent deformation characteristics of subgrade soils due to repeated loading," Transportation research record, no. 537 , 1975.

Q. Zhu, T. Li, X. Gao, Y. Wang, and B. Wang, "Deformation characteristics and failure evolution in deep high-stress roadways under creep action," Engineering Failure Analysis, vol. 154 , p. 107689 , 2023 .

S. Yin, J. n. Huang, X. Li, L. Bai, X. Zhang, and C. Li, "Experimental study on deformation characteristics and pore characteristics variation of granite residual soil," Scientific Reports, vol. 12 , no. 1 , p. 12314 , 2022 .

X. Zhang, Y. Gao, Y. Wang, Y.-z. Yu, and X. Sun, "Experimental study on compaction-induced anisotropic mechanical property of rockfill material, " Frontiers of Structural and Civil Engineering, vol. 15 , pp. 109 - 123 , 2021 .

X. Liu, D. Zou, J. Liu, and B. Zheng, "Predicting the small strain shear modulus of coarse-grained soils," Soil Dynamics and Earthquake Engineering, vol. 141 , pp. 106468 , 2021 .

J. Chen, Y. Zhang, Y. Yang, B. Yang, B. Huang, and X. Ji, "Influence of Coarse Grain Content on the Mechanical Properties of Red Sandstone Soil," Sustainability, vol. 15 , no. 4 , pp. 3117 , 2023 .

R. Haggerty, J. Sun, H. Yu, and Y. Li, "Application of machine learning in groundwater quality modeling-A comprehensive review," Water Research, vol. 233 , pp. 119745 , 2023 .

M. Abozraig, B. Ok, and A. Yildiz, "Determination of shear strength of coarse-grained soils based on their index properties: a comparison between different statistical approaches," Arabian Journal of Geosciences, vol. 15 , no. 7 , pp. 593 , 2022 .

C. C. Ikeagwuani, and D. C. Nwonu, "Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques," Innovative Infrastructure Solutions, vol. 7 , no. 4 , pp. 273 , 2022 .

P. Zhang, Z.-Y. Yin, Y.-F. Jin, and X.-F. Liu, "Modelling the mechanical behaviour of soils using machine learning algorithms with explicit formulations," Acta Geotechnica, pp. 1 - 20 , 2021 .

N. Zhang, S.-L. Shen, A. Zhou, and Y.-F. Jin, "Application of LSTM approach for modelling stress–strain behaviour of soil," Applied Soft Computing, vol. 100 , pp. 106959 , 2021

P. Zhang, and Z.-Y. Yin, "A novel deep learning-based modelling strategy from image of particles to mechanical properties for granular materials with CNN and BiLSTM," Computer Methods in Applied Mechanics and Engineering, vol. 382 , pp. 113858 , 2021 .

J. Lampinen, and A. Vehtari, "Bayesian approach for neural networks—review and case studies," Neural networks, vol. 14 , no. 3 , pp. 257 - 274 , 2001 .

Li Su, Guo Zhaochun, Wang Cong, Chen Tianen, and Yuan Zhigao, "A review of the application of information technology in crop pest and disease monitoring and early warning," Jiangsu Agricultural Sciences, vol. 46 , no. 22 , pp. 1 - 6 , 2018 .

P. Zhang, Z.-Y. Y in, and Y.-F. Jin, "Bayesian neural network-based uncertainty modelling: application to soil compressibility and undrained shear strength prediction," Canadian Geotechnical Journal, vol. 59 , no. 4 , pp. 546 - 557 , 2022 .

A. Bate, M. Lindquist, I. R. Edwards, S. Olsson, R. Orre, A. Lansner, and R. M. De Freitas, "A Bayesian neural network method for adverse drug reaction signal generation," European journal of clinical pharmacology, vol. 54 , pp. 315 - 321 , 1998 .

Li Mingxuan, Han Hongwei, Liu Haojie, Sang Wenjing, and Yuan Sanyi, "Permeability prediction and uncertainty estimation based on data distribution domain transformation and Bayesian neural networks," Chinese Journal of Geophysics, vol. 66 , no. 4 , pp. 1664 - 1680 , 2023 .

Bi Chunguang, Wang Jinlong, Hu Nan, Hu Qingxin, Huang Chengyu, and Han Yongqi, "Early warning model for maize diseases based on Bayesian neural networks," Journal of Jilin Agricultural University, vol. 43 , no. 2 , pp. 189 - 195 , 2021 .