首页|Reports Summarize Machine Learning Findings from Nanjing Tech University (Optimi zation of Mix Proportion and Strength Prediction of Magnesium Phosphate Cement-b ased Composites Based On Machine Learning)
Reports Summarize Machine Learning Findings from Nanjing Tech University (Optimi zation of Mix Proportion and Strength Prediction of Magnesium Phosphate Cement-b ased Composites Based On Machine Learning)
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NETL
NSTL
Elsevier
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news reporting from Nanjing, People’s Republic of China, by NewsRx journalists, research stated, “In order to optimize the mix ra tio design of magnesium phosphate cementitious composites (MPCC) to obtain excel lent workability and strength, this study investigated the effects of MgO-phosph ate ratio, water-binder ratio, aggregatebinder ratio, the mass ratio of coarse and fine aggregates, mineral admixture content and steel fiber content on the fl owability, setting time and cubic compressive strength (CCS) of MPCC using ortho gonal design of experiments with a total of 32 sets of mix ratios. The experimen tal results were also analyzed utilizing range and variance analysis to determin e the main influencing parameters and the optimal dosage of each factor, and the optimal MPCC mix ratio was obtained.”
NanjingPeople’s Republic of ChinaAsi aAnionsChemicalsCyborgsEmerging TechnologiesLight MetalsMachine Lear ningMagnesiumMagnesium PhosphatePhosphatesPhosphoric AcidsNanjing Tech University
NETL
NSTL
Elsevier
