论文标题
高保真地震强度度量,以评估尾矿中动态液化
A high-fidelity seismic intensity measure to assess dynamic liquefaction in tailings
论文作者
论文摘要
在动态条件下尾矿大坝的变形分析需要使用地震记录作为输入负载。此外,这些记录必须代表以地面运动功率指标表示强度度量(IM)表示的局部地震性。描述地震记录的特征的能力和准确性在地震工程和岩土工程设施的损害评估中起着基本作用。现有的IMS都不代表给定地震需求(例如残留位移)的足够强大的预测指标。不同的信号可能会产生广泛的结果,其效果不同,可能会对整体失败造成微不足道的损害,具体取决于结构。通常的工程程序选择了大量记录来克服此限制并开发大量数值模拟以限制结果的不确定性,这成为一种耗时的方法。本文提出了一种新的高保真地震IM,以执行更准确的地面运动{Selection},该动作捕获了大坝无法过滤的频率内容的记录的光谱特性。该IM代表了一种事先估算地震需求的方法,例如,在流离失所方面表示。提出的IM使用能够捕获动态液化的本构模型应用于上游尾矿横截面的有限元模型。获得的结果表明,我们的建议给出了与所选需求不同的高度可靠的相关性。还讨论了与古典IM的比较,这表明我们的建议是对我们社区中大量过时讨论的实用解决方案。
Deformation analyses of tailings dams under dynamic conditions require using earthquake records as input loading. Moreover, these records must represent the local seismicity, expressed by ground motion power indicators denominated intensity measures (IM). The ability and accuracy to describe the characteristics of a seismic record play a fundamental role in earthquake engineering and damage assessment of geotechnical facilities. None of the existing IMs represents a robust enough predictor of a given seismic demand (e.g., residual displacements). Different signals may generate a wide spectrum of results, with diverse effects that could produce insignificant damage to global failure depending on the structure. Usual engineering procedures select a huge number of records to overcome this limitation and develop a large set of numerical simulations to bound the uncertainty of the results, which becomes a time-consuming approach. This paper presents a new high-fidelity seismic IM to perform more accurate ground motion {selection}, which captures the spectral properties of the record for the frequency content that the dam does not filter. This IM represents a way to estimate beforehand a seismic demand, expressed, for instance, in terms of displacements. The proposed IM is applied to a finite element model for an upstream tailings dam cross-section, using a constitutive model capable of capturing dynamic liquefaction. The obtained results show that our proposal gives highly reliable correlations with different selected demands. Comparisons with classical IMs are also discussed, showing that our proposal emerges as a practical solution to a large dated discussion within our community.