首页|Findings from Dong-A University Yields New Findings on Machine Learning (Enginee ring Punching Shear Strength of Flat Slabs Predicted By Nature-inspired Metaheur istic Optimized Regression System)
Findings from Dong-A University Yields New Findings on Machine Learning (Enginee ring Punching Shear Strength of Flat Slabs Predicted By Nature-inspired Metaheur istic Optimized Regression System)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news originating from Da Nang, Vietnam, by NewsRx corre spondents, research stated, "Reinforced concrete (RC) flat slabs, a popular choi ce in construction due to their flexibility, are susceptible to sudden and britt le punching shear failure. Existing design methods often exhibit significant bia s and variability." Financial support for this research came from Seoul National University of Scien ce and Technology (SeoulTech) - Seoul National University of Science and Technol ogy (SeoulTech). Our news journalists obtained a quote from the research from Dong-A University, "Accurate estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and management. This study introduces a nove l computation method, the jellyfish-least square support vector machine (JSLSSV R) hybrid model, to predict punching shear strength. By combining machine learni ng (LSSVR) with jellyfish swarm (JS) intelligence, this hybrid model ensures pre cise and reliable predictions. The model's development utilizes a real-world exp erimental data set. Comparison with seven established optimizers, including arti ficial bee colony (ABC), differential evolution (DE), genetic algorithm (GA), an d others, as well as existing machine learning (ML)-based models and design code s, validates the superiority of the JS-LSSVR hybrid model."
Da NangVietnamAsiaCyborgsEmergin g TechnologiesEngineeringMachine LearningDong-A University
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