首页|Recent Studies from Islamic Azad University Add New Data to Machine Learning (Stacking Ensemble-Based Machine Learning Model for Predicting Deterioration Components of Steel W-Section Beams)
Recent Studies from Islamic Azad University Add New Data to Machine Learning (Stacking Ensemble-Based Machine Learning Model for Predicting Deterioration Components of Steel W-Section Beams)
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Data detailed on artificial intelligence have been presented. According to news reporting originating from Zanjan, Iran, by NewsRx correspondents, research stated, “The collapse evaluation of the structural systems under seismic loading necessitates identifying and quantifying deterioration components (DCs).” Our news journalists obtained a quote from the research from Islamic Azad University: “In the case of steel w-section beams (SWSB), three distinct types of DCs have been derived. These deterioration components for steel beams comprise the following: pre-capping plastic rotation (thp), post-capping plastic rotation (thpc), and cumulative rotation capacity (L). The primary objective of this research is to employ a machine learning (ML) model for accurate determination of these deterioration components. The stacking model is a powerful combination of meta-learners, which is used for better learning and performance of base learners. The base learners consist of AdaBoost, Random Forest (RF), and XGBoost. Among various machine learning algorithms, the stacking model exhibited superior functioning.”
Islamic Azad UniversityZanjanIranAsiaCyborgsEmerging TechnologiesMachine Learning
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