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<dc:title xml:lang="pl"><![CDATA[Artificial Neural Network-aided Mathematical Model for Predicting Soil Stress-strain Hysteresis Loop Evolution]]></dc:title>
<dc:creator><![CDATA[Bocheńska, Marta]]></dc:creator>
<dc:creator><![CDATA[Srokosz, Piotr Emanuel]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[deep neural networks]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[hysteresis loops]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[mathematical modeling]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[soil stress-strain curves]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[cyclic loading]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[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.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[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.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[tytuł dodatkowy: Prace z Inżynierii Lądowej i Środowiska]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego]]></dc:publisher>
<dc:contributor><![CDATA[Kuczyński, Tadeusz - red.]]></dc:contributor>
<dc:date><![CDATA[2024]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
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<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/Content/96936/ceer_2024_3_8_bochenska_artificial.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/dlibra/publication/108768/edition/96936/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:96936]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[Civil and Environmental Engineering Reports (CEER), no 34, vol. 3]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:108768]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
<dc:rights xml:lang="pl"><![CDATA[CC 4.0]]></dc:rights>
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