摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-调查人员发布了关于马学习的新报告。根据NewsRx记者从上海发回的新闻报道，近几十年来，基于性能的D级地震工程(PBEE)以概率地震易损性评估(PSFA)为主要研究内容，对地震决策具有不可替代的意义，在实施PSFA的多种途径中，"经典的线性回归方法(LRM)被认为是应用最广泛的方法之一。"本研究的资金来源包括国家重点研发计划、国家自然科学基金(NSFC)。我们的新闻编辑引用了同济大学的一篇研究，“然而，一般LRM在fr敏捷性曲线组上采用分位数回归方法(QRM)来近似结构脆弱性在一定烈度测度(IM)下的确定性概率密度分布(PSD)，从而，”针对QRM推导的地震易损性表示由于忽略了随机地震动的特殊性而不够可信的问题,本文提出了一种融合物理模型和机器学习模型的快速地震动自适应概率地震易损性评估(GmaPSFA),通过复杂的框架设计和新的参数估计,实现了地震易损性的快速评估。为了验证该方法的有效性，在六层框架结构上，采用了LRM和GmaPSFA，提出了一种融合APDL和编码语言的全自动批处理方法。
Abstract
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."
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