首页|Researcher from University of Brasilia Details New Studies and Findings in the A rea of Machine Learning (A comparison between geomembrane-sand tests and machine learning predictions)
Researcher from University of Brasilia Details New Studies and Findings in the A rea of Machine Learning (A comparison between geomembrane-sand tests and machine learning predictions)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – A new study on artificial intelligence is now ava ilable. According to news reporting from Brasilia, Brazil, by NewsRx journalists , research stated, “The interaction between soils and geosynthetics plays an imp ortant role in the applications of these materials for reinforcement in geotechn ical engineering.” The news correspondents obtained a quote from the research from University of Br asilia: “The complexities of soil-geosynthetic interactions vary depending on th e type and properties of both the geosynthetic and the soil. This paper introduc es a machine learning approach, specifically a random forest algorithm, for pred icting interface friction angles. The dataset comprises 495 interfaces involving geomembranes and sand, with fourteen influencing parameters recorded for each i nterface, influencing the shear strength outcome. In the analysis, Pearson’s cor relation coefficient is employed to measure the linear interdependence between e ach pair of input-input and input-output variables. Following the linear regress ion analysis, an optimized random forest is utilized to project the interface fr iction angle. The random forest algorithm divides the selected data into trainin g and testing sets, and only 3% of the training set and 6% of the testing set exceed ±5° from the actual records.”
University of BrasiliaBrasiliaBrazilSouth AmericaAlgorithmsCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
