首页|New Machine Learning Data Have Been Reported by Researchers at Tongji University (Fusing Physics-based and Machine Learning Models for Rapid Ground-motion-adapt ative Probabilistic Seismic Fragility Assessment)
New Machine Learning Data Have Been Reported by Researchers at Tongji University (Fusing Physics-based and Machine Learning Models for Rapid Ground-motion-adapt ative Probabilistic Seismic Fragility Assessment)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, "Performance -base d earthquake engineering (PBEE) has asserted probabilistic seismic fragility ass essment (PSFA) as the main research content in light of its irreplaceable signif icance for seismic decision -making in recent decades. Among the multiple approa ches of PSFA implementation, the classical linear regression method (LRM) domina tes over practice regarded as one of the most widely -used." Funders for this research include National Key R&D Program of China , National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from Tongji University, "How ever, the general LRM adopts quantile regression method (QRM) on the group of fr agility curves to approximate a deterministic probability density distribution ( PSD) of structural fragility against certain intensity measure (IM) of potential ly confronting earthquake. Consequently, the QRM-derived fragility representatio n might not be credible enough while evaluating a newly -occurred seismic event owing to its neglect of specificity of stochastic ground motion. To address this issue, a fusing physics -based and machine learning models towards rapid ground -motion-adaptative probabilistic seismic fragility assessment (GmaPSFA) is propo sed in present study. With sophisticated framework design and novel fragility hy perparameters estimation, the involved design philosophy and mechanism translati ng are both elaborated. To validate the method, both the LRM and GmaPSFA were co nducted on a six -story frame structure, where a novel fully -automatic batch pr ocessing approach fusing APDL and coding languages was propounded for structural analysis."
ShanghaiPeople's Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningTongji University
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