首页|Researchers at Polytechnic University Torino Release New Data on Machine Learnin g (Machine Learning Modelling of Structural Response for Different Seismic Signa l Characteristics: a Parametric Analysis)
Researchers at Polytechnic University Torino Release New Data on Machine Learnin g (Machine Learning Modelling of Structural Response for Different Seismic Signa l Characteristics: a Parametric Analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news reporting originating in Turin, Italy, by NewsRx j ournalists, research stated, "The present study investigates the best seismic pa rameters for modeling the dynamic response of various nonlinear structural syste ms by comparing different Machine Learning (ML) algorithms. A total of 400 synth etic excitations were generated and analyzed against 23 seismic parameters." Financial support for this research came from European Union-Next Generation E U. The news reporters obtained a quote from the research from Polytechnic University Torino, "These signals were used in a step-by-step numerical analysis to calcu late the dynamic responses of 1000 singledegree- of-freedom (SDOF) systems with varying mechanical properties. The data obtained from these responses were proce ssed using 20 ML algorithms, including linear regression, tree, support vector m achine (SVM), boosted and bagged trees, and artificial neural network (ANN). Eac h ML algorithm used a single seismic parameter as input to determine the most pr edictive parameters for modeling structural responses, defining the high predict ive seismic parameters (HPSP) set. To validate the obtained results, the most ef fective model predictions have been compared with the results of the parametric step-by-step analyses performed for a new group of natural ground motions. The f indings demonstrate that with a properly calibrated training phase, considering the specific site hazard and selecting seismic parameters from the HPSP set, the ML model can accurately estimate seismic responses whit a significantly reduced computational effort."
TurinItalyEuropeCyborgsEmerging TechnologiesMachine LearningPolytechnic University Torino
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