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Significance of quantifying uncertainties in probabilistic modeling and a possible approach to select the best: A study using SPT and CPT based liquefaction case histories

机译:量化概率建模中不确定性的意义及选择最佳方法:使用SPT和基于CPT的液化案历史研究

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There have been several statistical methods developed to evaluate the probability of seismically induced soil liquefaction. Among these methods, the logistic regression has been the most widely used method to model the probability of liquefaction using the Standard Penetration Test (SPT) and Cone Penetration Test (CPT) based case histories. However, the predicted probabilities using logistic regression can be different based on the uncertainties related to distribution of explanatory variables, significance of coefficient of explanatory variables, and distribution of liquefaction to non-liquefaction case histories. In this paper, a possible approach has been developed to select the logistic regression model that best addresses these uncertainties for SPT and CPT based case histories using various statistical tests. This approach is developed for the most updated CPT based case histories from Ku et al. (2012), and SPT based case histories from Idriss et al. (2010). Further, the logistic regression model is compared with the existing probabilistic models for CPT and SPT based case histories. This study shows that without considering the aforementioned uncertainties, the probability values can be significantly different and can give false sense of risk of liquefaction for a particular site of interest.
机译:已经开发了几种统计方法来评估地震诱导的土壤液化的可能性。在这些方法中,Logistic回归是使用标准穿透试验(SPT)和基于锥形渗透测试(CPT)的案例历史来模拟液化概率最广泛使用的方法。然而,使用逻辑回归的预测概率可以基于与解释变量分布的不确定性,解释性变量系数的意义,以及对非液化案历史的液化分布的不确定性。在本文中,已经开发了一种可能的方法来选择最能使用各种统计测试来解决基于SPT和CPT的案例历史的这些不确定性的逻辑回归模型。这种方法是为来自Ku等人的最新的CPT基于CPT的案例历史而开发。 (2012),idriss等人的基于SPT的案例历史。 (2010)。此外,将逻辑回归模型与用于CPT和基于SPT的案例历史的现有概率模型进行比较。本研究表明,在不考虑上述不确定性的情况下,概率值可以显着不同,并且可以给出特定感兴趣部位液化风险的虚假风险。

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