首页|Islamic Azad University Researchers Further Understanding of Machine Learning (I n-depth simulation of netted collars on scour depth control using machine-learni ng models)
Islamic Azad University Researchers Further Understanding of Machine Learning (I n-depth simulation of netted collars on scour depth control using machine-learni ng models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting from Ahvaz, Iran, by NewsRx journalists, research stated, "The present research aims to forecast the safegu arding efficacy of a mesh collar, of hole diameter d, in developing of scour dep th around a cylindrical bridge pier of diameter D under the steady and clean wat er conditions utilizing three machine learning models (MLMs), namely Support Vec tor Machine (SVM), Gene Expression Programming (GEP), and Multilayer Perceptron (MLP)." The news editors obtained a quote from the research from Islamic Azad University : "A total of 240 laboratory measured scour depth data were employed in this stu dy. The experimental setup involved the installation of four distinct mesh colla rs, configured in the shapes of a circle, square, rectangle, an d triangle by sh ape factor (SF) of 1.78, 1, 2.3, and 1.69, respectively. The mean size of non-co hesive sand particles was selected with a particle size of 1.3 mm. Employing dim ensional analysis, three dimensionless parameters, namely SF, d/D, and Uc/U were identified as independent variables adopting for the input variables for the ML Ms. The performance assessment metrics involved Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2), and the Developed Discrepancy Ratio (DDR). The simulation results demonstrated that MLMs exhibit a high degree of accuracy in predicting relative scour depth (RSD) influenced by the presence of mesh collars."
Islamic Azad UniversityAhvazIranAs iaCyborgsEmerging TechnologiesMachine Learning
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