Investigating A Hybrid Extreme Learning Machine Coupled With Dingo Optimization Algorithm For Modeling Liquefaction Triggering In Sand-silt Mixtures

This article discusses the use of non-linear normalization techniques in machine learning (ML) model development and compares their predictive accuracy to traditional linear normalization methods. The study also introduces a graphical user interface (GUI) to aid in utilizing the developed model. Additionally, the article explores the energy dissipation theories and parameters used to predict soil composition and liquefaction resistance in sand-silt mixtures.