首页|Recent Studies from Harran University Add New Data to Machine Learning (Predicti ng Main Characteristics of Reinforced Concrete Buildings Using Machine Learning)
Recent Studies from Harran University Add New Data to Machine Learning (Predicti ng Main Characteristics of Reinforced Concrete Buildings Using Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily NewsResearch findings on artificial intelligence are discussed in a new report. According to news originating from Sanliurfa, Turkey, by NewsRx correspondents, research stated, "This paper presents a comprehensive study of five machine learning (ML) algorithms for predicting key characteristi cs of Reinforced Concrete (RC) structural systems." Our news reporters obtained a quote from the research from Harran University: "A novel dataset, ModRes, consisting of 9723 examples derived from modal and respo nse spectrum analyses on masonryinfilled three-dimensional RC buildings, was cr eated for ML applications. The primary objective is to develop an ML model using five distinct algorithms from the literature, capable of concurrently predictin g torsional irregularity, modal participating mass ratio (MPMR), and the fundame ntal period in a 3D environment, while accounting for the influence of infill wa lls. Additionally, the study aims to determine the applicability of pushover ana lysis (POA) without the need for extensive numerical modeling and analysis. This approach optimizes the preliminary design process with minimal computational ef fort, providing valuable insights into dynamic and torsional responses during se ismic events. The Categorical Boosting algorithm demonstrated outstanding perfor mance, achieving R2 values of 0.977 for torsional irregularity, 0.997 for the fu ndamental period, and 0.923 for MPMR on the test dataset."
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