Bocheńska, Marta ; Srokosz, Piotr Emanuel
Contributor: Title: Group publication title: Subject and Keywords:deep neural networks ; hysteresis loops ; mathematical modeling ; soil stress-strain curves ; cyclic loading
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 R? value of 0.9788 for testing and 0.9944 for training.
Description:tytuł dodatkowy: Prace z Inżynierii Lądowej i Środowiska
Publisher:Zielona Góra: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
Date: Resource Type: Format: DOI: Pages: Source:Civil and Environmental Engineering Reports (CEER), no 34, vol. 3
Language: License: License CC BY 4.0: Rights: