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Site response implications associated with using non-unique Vs profiles from surface wave inversion in comparison with other commonly used methods of accounting for Vs uncertainty

机译:与其他常用的解决Vs不确定性的方法相比,与使用表面波反演的非唯一Vs剖面相关的站点响应含义

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This paper discusses variability and accuracy of site response predictions performed using shear wave velocity (Vs) profiles derived from non-unique surface wave inversions and other commonly used statistical methods of accounting for epistemic uncertainty and aleatory variability in Vs. Specifically, linear and equivalent linear site response analyses were performed on the following three classes of Vs profiles: (1) 350 Vs profiles developed by performing multiple surface wave inversions, each with a unique set of layering parameters, on a common dispersion dataset, (2) two upper/lower range base-case Vs profiles developed by systematically increasing or decreasing the solution Vs profile by 20%, and (3) 100 Vs profiles developed using the Vs randomization procedure proposed by Toro (1995) [26]. Vs profiles derived from surface wave inversions generally yielded accurate site response estimates with minimal variability, so long as their theoretical dispersion data fit the experimental dispersion data well. On the other hand, the upper/lower range and randomized Vs profiles generally produced inaccurate and highly variable site response predictions, although the inclusion of site-specific parameters in the randomization model improved the results. At real sites where substantial aleatory variability is anticipated and/or the epistemic uncertainty is quite high, the site response estimates associated with the randomized and/or upper/lower range Vs profiles may be deemed acceptable. However, if the experimental dispersion data and horizontal-to-vertical spectral ratios are shown to be consistent over the footprint of a site, it may be possible to significantly reduce the uncertainty associated with the input Vs profile and the resulting uncertainty in the site response. (C) 2016 Elsevier Ltd. All rights reserved.
机译:本文讨论了使用从非唯一表面波反演获得的剪切波速度(Vs)剖面图和其他常用的统计方法来解决站点响应预测的变异性和准确性,这些统计方法解释了VS中的认知不确定性和偶然变异性。具体来说,对以下三类Vs轮廓进行线性和等效线性部位响应分析:(1)通过在公共色散数据集上执行多个表面波反演而开发的350个Vs轮廓,每个表面波都具有唯一的分层参数集, 2)通过系统地增加或减少20%的解决方案Vs轮廓来开发两个上/下范围的基本Vs轮廓,以及(3)使用Toro(1995)提出的Vs随机化程序开发的100 Vs轮廓[26]。只要其理论色散数据与实验色散数据完全吻合,从表面波反演得出的Vs剖面通常会产生准确的位置响应估计值,且变异性最小。另一方面,尽管在随机化模型中包括了特定于位点的参数,但上/下范围和随机化的Vs轮廓通常会产生不准确且高度可变的位点响应预测。在预期发生重大偶然变化和/或认知不确定性非常高的真实场所,与随机和/或上/下范围Vs轮廓相关的场所响应估计值可以被认为是可以接受的。但是,如果实验色散数据和水平与垂直光谱之比在站点的覆盖范围内保持一致,则可能会显着减少与输入Vs轮廓相关的不确定性以及由此产生的站点响应中的不确定性。 (C)2016 Elsevier Ltd.保留所有权利。

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