首页|Researchers from Beijing University of Technology Report Findings in Machine Lea rning (Multi-objective Optimization Design of Recycled Aggregate Concrete Mixtur e Proportions Based On Machine Learning and Nsga-ii Algorithm)
Researchers from Beijing University of Technology Report Findings in Machine Lea rning (Multi-objective Optimization Design of Recycled Aggregate Concrete Mixtur e Proportions Based On Machine Learning and Nsga-ii Algorithm)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on Machine Learn ing. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “This paper employs Support Vector Regression ( SVR), Random Forest Regression (RF), Gradient Boosting (GB), and Extreme Gradien t Boosting (XGB) algorithms to establish the compressive strength prediction mod els for Recycled Aggregate Concrete (RAC) and analyze the influence of ten input s on RAC compressive strength. Combined with the best prediction model, the Non- dominated Sorting Genetic Algorithm II (NSGA-II) is applied for multiobjective optimization of mixture proportions in RAC addressing cost, carbon emissions, an d compressive strength as key objectives.” Financial supporters for this research include National Natural Science Foundati on of China-China National Railway Group Co., Ltd. Railway Basic Research Joint Fund Project, Beijing Natural Science Foundation.
BeijingPeople’s Republic of ChinaAsi aAlgorithmsCyborgsEmerging TechnologiesMachine LearningBeijing Univers ity of Technology
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