首页|New Machine Learning Study Findings Have Been Reported by Investigators at University of Bremen (Chained Machine Learning Model for Predicting Load Capacity and Ductility of Steel Fiberreinforced Concrete Beams)
New Machine Learning Study Findings Have Been Reported by Investigators at University of Bremen (Chained Machine Learning Model for Predicting Load Capacity and Ductility of Steel Fiberreinforced Concrete Beams)
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NETL
NSTL
Wiley
Data detailed on Machine Learning have been presented. According to news reporting originating in Bremen, Germany, by NewsRx journalists, research stated, "One of the main issues associated with steel fiber-reinforced concrete (SFRC) beams is the ability to anticipate their flexural response. With a comprehensive grid search, several stacked models (i.e., chained, parallel) consisting of various machine learning (ML) algorithms and artificial neural networks (ANNs) were developed to predict the flexural response of SFRC beams." Funders for this research include University of Bremen, National Research Foundation of Korea, Ministry of Science & ICT (MSIT), Republic of Korea, Yonsei University. The news reporters obtained a quote from the research from the University of Bremen, "The flexural performance of SFRC beams under bending was assessed based on 193 experimental specimens from reallife beam models. The ML techniques were applied to predict SFRC beam responses to bending load as functions of the steel fiber properties, concrete elastic modulus, beam dimensions, and reinforcement details. The accuracy of the models was evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of actual versus predicted values. The findings revealed that the proposed technique exhibited notably superior performance, delivering faster and more accurate predictions compared to both the ANNs and parallel models. Shapley diagrams were used to analyze variable contributions quantitatively. Shapley values show that the chained model prediction of ductility index is highly affected by two other targets (peak load and peak deflection) that show the chained algorithm utilizing the prediction of previous steps for enhancing the prediction of the target feature."
BremenGermanyEuropeCyborgsEmerging TechnologiesMachine LearningUniversity of Bremen
NETL
NSTL
Wiley
