首页|Ho Chi Minh City University of Technology Researchers Report Recent Findings in Machine Learning (A machine learning regression approach for predicting uplift c apacity of buried pipelines in anisotropic clays)
Ho Chi Minh City University of Technology Researchers Report Recent Findings in Machine Learning (A machine learning regression approach for predicting uplift c apacity of buried pipelines in anisotropic clays)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news originating from Ho Chi Minh City, Vi etnam, by NewsRx correspondents, research stated, "The uplift capacity of pipeli ne systems in geotechnical engineering is influenced by internal loading and ext ernal factors, making it a significant consideration in pipeline design problems ." The news journalists obtained a quote from the research from Ho Chi Minh City Un iversity of Technology: "Previous research has conducted experimental tests and numerical solutions to investigate the relationship between force and displaceme nt or the resistance of pipelines in numerous soil media. This paper proposes a machine-learning regression technique to predict the uplift capacity of buried p ipelines in anisotropic clays with parametric analysis. Specifically, the Multiv ariate Adaptive Regression Spline (MARS) is employed to establish the relationsh ip between input parameters, namely the depth ratio (H/D), anisotropic strength ratio (re), load inclination (b), overburden factor (gH/Suc), adhesion factor (a ), and the output uplift resistance (N) obtained from the finite element limit a nalysis (FELA), utilizing the AUS material model integrated with the OptumG2 com mercial program. Furthermore, the sensitivity analysis outcome shows the embedde d depth ratio is the most critical parameter, followed by the anisotropic streng th ratio, overburden factor, load inclination, and adhesion factor. Additionally , the shear velocity field contours show that when the depth ratio and the load inclination increase, the dissipation of shear changes."
Ho Chi Minh City University of Technolog yHo Chi Minh CityVietnamAsiaCyborgsEmerging TechnologiesMachine Lear ning
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