首页|Research Conducted at University of Toronto Has Provided New Information about M achine Learning (Exploring Machine Learning To Study and Predict the Chloride Th reshold Level for Carbon Steel Reinforcement)
Research Conducted at University of Toronto Has Provided New Information about M achine Learning (Exploring Machine Learning To Study and Predict the Chloride Th reshold Level for Carbon Steel Reinforcement)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Data detailed on Machine Learning have been presented. According to news reportingoriginating from Toronto, Canada, b y NewsRx correspondents, research stated, “Chloride-induced corrosionof steel r einforcing bar (rebar) is the primary cause of deterioration in reinforced concr ete structures, posinga significant infrastructure challenge. The chloride thre shold level (CTL) of rebar, which represents thecritical amount of chloride nee ded to initiate active corrosion, is crucial in corrosion and service life prediction models.”
TorontoCanadaNorth and Central Ameri caAnionsChloridesCyborgsEmerging TechnologiesHydrochloric AcidMachin e LearningSupport Vector MachinesVector MachinesUniversity of Toronto
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