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Quantification of model uncertainty and variability in Newmark displacement analysis

机译:纽马克排量分析模型不确定性与变异性的量化

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摘要

Newmark displacement model has been extensively used to evaluate earthquake-induced displacement in earth systems. In this paper, model uncertainty and variability associated with the Newmark displacement analysis are systematically studied. Fourteen Newmark displacement models using scalar or vector intensity measures (IMs) as predictors are compared in this study. In general, model uncertainty for the vector-IM models is found smaller than that of the scalar-IM models, and remains consistent over different earthquake magnitude, distance and site conditions. Yet, the model uncertainty of these Newmark displacement models is still much larger than that of the ground-motion prediction equations (GMPEs) for IMs, indicating further development of the models is much needed. Considering the variabilities contributed from both GMPEs and Newmark displacement models, the total variability of the predicted Newmark displacements is rather consistent among the scalar- and vector-IM displacement models, due to extra sources of variability introduced by incorporating additional IMs. Finally, a logic tree scheme is implemented in the fully probabilistic Newmark displacement analysis to account for the model uncertainty and variability. Sensitivity analysis shows that specific weights would not significantly influence the displacement hazard curves as the results may be dominated by outlier models. Instead, selecting appropriate GMPEs and Newmark displacement models is more important in using the logic-tree framework.
机译:纽马克排量模型已被广泛地用于评估地球系统中的地震诱导的位移。在本文中,系统地研究了与纽马克位移分析相关的模型不确定性和可变性。在本研究中比较了使用标量或向量强度测量(IMS)作为预测器的14个纽马克排量模型。通常,发现矢量-im模型的模型不确定度小于标量 - IM模型的模型不确定性,并且在不同的地震幅度,距离和现场条件下保持一致。然而,这些纽马克位移模型的模型不确定性仍然大于IMS的地面运动预测等式(GMPE)的模型不确定性,表明需要更需要进一步发展模型。考虑到GMPES和纽马克位移模型的变量,由于通过结合额外IMS引入的额外可变性来源,预测的纽马克位移的总变化在标量和矢量 - IM位移模型中是相当一致的。最后,在完全概率的纽约标记位移分析中实现了逻辑树方案,以解释模型不确定性和可变性。敏感性分析表明,当结果可能由异常型号主导时,特定权重不会显着影响位移危险曲线。相反,选择适当的GMPE和Newmark位移模型在使用逻辑树框架时更为重要。

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