首页|Researchers at Wuhan University Release New Data on Machine Learning (A Methodol ogy for Stress-strain Behavior Characterization and Mixture Optimization of Recy cled Aggregate Concrete Based On Machine Learning)
Researchers at Wuhan University Release New Data on Machine Learning (A Methodol ogy for Stress-strain Behavior Characterization and Mixture Optimization of Recy cled Aggregate Concrete Based On Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting originating in Wuhan,People's Repub lic of China,by NewsRx journalists,research stated,"The characterization of s tress-strain behaviors of recycled aggregate concrete (RAC) excluding physical p roperties of recycled aggregates (RAs) may result in an inaccurate prediction of mechanical responses in practical applications. In this study,a data-driven mo del using a refined long short-term memory (LSTM) network is established based o n the Bayesian optimization algorithm,with the motivation to accurately predict the uniaxial compressive stress-strain behaviors of RAC,including the stress-s train relation,elastic modulus,peak stress,and the peak strain." Funders for this research include National Natural Science Foundation of China ( NSFC),Key Research and Development Program of Hubei Province,China,Guangdong Basic and Applied Basic Research Foundation. The news reporters obtained a quote from the research from Wuhan University,"Tr aining and testing of the proposed model require the integration of the mixture content and the fundamental physical properties of RAs with the stress-strain re lation of ordinary concrete featured by prominent sequential attributes. Upon a dataset containing 100 experimental samples from independent studies,covering a wide range of RA substitution rates,the superior prediction capability of the proposed LSTM network is demonstrated in comparison with the analytical results of three empirical mechanics-driven models. Finally,the trained LSTM network is further employed to optimize the mixture for RAC using the Bayesian optimizatio n technique innovatively,to achieve a balance between the mechanical performanc e and requirement to the quality of RAs."
WuhanPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningWuhan University
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