首页|Research Findings from Ruhr-Universitat Bochum Update Understanding of Machine Learning (Influence of Sampling Methods on the Accuracy of Machine Learning Predictions Used for Strain- Dependent Slope Stability)
Research Findings from Ruhr-Universitat Bochum Update Understanding of Machine Learning (Influence of Sampling Methods on the Accuracy of Machine Learning Predictions Used for Strain- Dependent Slope Stability)
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Current study results on artificial intelligence have been published. According to news reporting out of Bochum, Germany, by NewsRx editors, research stated, “Supervised machine learning (ML) techniques have been widely used in various geotechnical applications.” The news reporters obtained a quote from the research from Ruhr-Universitat Bochum: “While much attention is given to the ML techniques and the specific geotechnical problem being addressed, the influence of sampling methods on ML performance has received relatively less scrutiny. This study applies supervised ML to the strain-dependent slope stability (SDSS) method for the prediction of the factor of safety (FoS) using hypoplasticity. It delves into different sampling strategies for training the ML model, emphasizing predictions of soil behavior in lower stress ranges. A novel sampling method is introduced to ensure a more representative distribution of samples in these ranges, which is challenging to achieve through traditional sampling approaches. The ML models were trained using traditional and modified sampling methods. Subsequently, slope stability analyses using SDSS were conducted with ML models trained from six different sampling methods.”
NETL
NSTL
Mdpi
