首页|Researchers from University of Texas San Antonio Discuss Findings in Machine Learning (Machine Learning Tools To Improve Nonlinear Modeling Parameters of Rc Columns)
Researchers from University of Texas San Antonio Discuss Findings in Machine Learning (Machine Learning Tools To Improve Nonlinear Modeling Parameters of Rc Columns)
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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 reportingfrom San Antonio, Texas, by NewsRx journalists, research stated, “Modeling parameters are essential to thefidelity of nonlinear models of concrete structures subjected to earthquake ground motio ns, especially whensimulating seismic events strong enough to cause collapse. T his paper addresses two of the most significantbarriers to improving nonlinear modeling provisions in seismic evaluation standards using experimental datasets : identifying the most likely mode of failure of structural components, and impl ementing data fittingtechniques capable of recognizing interdependencies betwee n input parameters and nonlinear relationshipsbetween input parameters and mode l outputs.”The news correspondents obtained a quote from the research from the University o f Texas San Antonio,“Machine learning tools in the Scikit-learn and Pytorch lib raries were used to calibrate equations and black-box numerical models for nonl inear modeling parameters (MP) a and b of reinforced concrete columnsdefined in the ASCE 41 and ACI 369.1 standards, and to estimate their most likely mode of failure. It wasfound that machine learning regression models and machine learni ng black -boxes were more accurate thancurrent provisions in the ACI 369.1/ASCE 41 Standards. Among the regression models, Regularized LinearRegression was th e most accurate for estimating MP a, and Polynomial Regression was the most accurate for estimating MP b. The two black -box models evaluated, namely the Gaussi an Process Regressionand the Neural Network (NN), provided the most accurate es timates of MPs a and b. The NN model wasthe most accurate machine learning tool of all evaluated.”
San AntonioTexasUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Texas San Antonio
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