ORIGINAL ARTICLE
Artificial Neural Network-aided Mathematical Model for Predicting Soil Stress-strain Hysteresis Loop Evolution
1
Faculty of Geoengineering
Institute of Geodesy and Civil Engineering, University of Warmia and Mazury in Olsztyn, Poland
Submission date: 2024-04-19
Final revision date: 2024-06-20
Acceptance date: 2024-07-05
Online publication date: 2024-07-19
Publication date: 2024-07-19
Corresponding author
Marta Bocheńska
Faculty of Geoengineering Institute of Geodesy and Civil Engineering, University of Warmia and Mazury in Olsztyn, Prawocheńskiego 15, 10-720, Olsztyn, Poland
Faculty of Geoengineering Institute of Geodesy and Civil Engineering, University of Warmia and Mazury in Olsztyn, Prawocheńskiego 15, 10-720, Olsztyn, Poland
Civil and Environmental Engineering Reports 2024;34(3):120-135
KEYWORDS
TOPICS
ABSTRACT
This study presents a novel approach to forecasting the evolution of hysteresis stress-strain response of different types of soils under repeated loading-unloading cycles. The forecasting is made solely from the knowledge of soil properties and loading parameters. Our approach combines mathematical modeling, regression analysis, and Deep Neural Networks (DNNs) to overcome the limitations of traditional DNN training. As a novelty, we propose a hysteresis loop evolution equation and design a family of DNNs to determine the parameters of this equation. Knowing the nature of the phenomenon, we can impose certain solution types and narrow the range of values, enabling the use of a very simple and efficient DNN model. The experimental data used to develop and test the model was obtained through Torsional Shear (TS) tests on soil samples. The model demonstrated high accuracy, with an average R² value of 0.9788 for testing and 0.9944 for training.
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| eISSN: | 2450-8594 |
| ISSN: | 2080-5187 |
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