首页|Universidad Pontificia Bolivariana Researcher Targets Machine Learning (Machine- Learning-Based Predictive Models for Compressive Strength, Flexural Strength, an d Slump of Concrete)
Universidad Pontificia Bolivariana Researcher Targets Machine Learning (Machine- Learning-Based Predictive Models for Compressive Strength, Flexural Strength, an d Slump of Concrete)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on artificial in telligence have been published. According to news originating from Medellin, Col ombia, by NewsRx editors, the research stated, “The process of concrete producti on involves mixing cement, water, and other materials.” Financial supporters for this research include Minciencias Colombia. The news correspondents obtained a quote from the research from Universidad Pont ificia Bolivariana: “The quantity of each of these materials results in a perfor mance that is particularly estimated in terms of compressive or flexural strengt h. It has been observed that the final performance of concrete has a high varian ce and that traditional formulation methods do not guarantee consistent results. Consequently, designs tend to be over-designed, generating higher costs than re quired, to ensure the performance committed to the client. This study proposes t he construction of predictive machine learning models to estimate compressive or flexural strength and concrete slump. The study was carried out following the T eam Data Science Process (TDSP) methodology, using a dataset generated by the Co lombian Ready Mix (RMX) company Cementos Argos S.A. over five years, containing the quantity of materials used for different concrete mixes, as well as performa nce metrics measured in the laboratory.”
Universidad Pontificia BolivarianaMede llinColombiaSouth AmericaCyborgsEmerging TechnologiesMachine Learning
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