首页|Future University Reports Findings in Machine Learning (Machine learning base models to predict the punching shear capacity of posttensioned UHPC flat slabs)
Future University Reports Findings in Machine Learning (Machine learning base models to predict the punching shear capacity of posttensioned UHPC flat slabs)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news originating from New Cairo, Egypt, by NewsRx correspondents, research stated, “The aim of this research is to present correction factors for the punching shear formulas of ACI-318 and EC2 design codes to adopt the punching capacity of post tensioned ultra-high-performance concrete (PT-UHPC) flat slabs. To achieve that goal, the results of previously tested PT-UHPC flat slabs were used to validate the developed finite element method (FEM) model in terms of punching shear capacity.” Financial support for this research came from Future University in Egypt. Our news journalists obtained a quote from the research from Future University, “Then, a parametric study was conducted using the validated FEM to generate two databases, each database included concrete compressive strength, strands layout, shear reinforcement capacity and the aspect ratio of the column besides the correction factor (the ratio between the FEM punching capacity and the design code punching capacity). The first considered design code in the first database was ACI-318 and in the second database was EC2. Finally, there different ‘Machine Learning’ (ML) techniques manly ‘Genetic programming’ (GP), ‘Artificial Neural Network’ (ANN) and ‘Evolutionary Polynomial Regression’ (EPR) were applied on the two generated databases to predict the correction factors as functions of the considered parameters.”
New CairoEgyptAfricaCyborgsEmerging TechnologiesMachine Learning
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