改进的概率地震需求-烈度关系：异方差方法
Improved probabilistic seismic demand-intensity relationship: heteroskedastic approachs
摘要：
概率地震需求-烈度关系作为结构抗震性能评估的重要组成部分，得到了广泛的研究。本文系统地研究了概率地震需求模型中的异方差现象。本文简要回顾了异方差的定义、诊断和常规处理方法，并在此基础上提出了两种适用于单变量和多变量情况的更通用的方法。以一座典型的四跨简支梁桥为研究对象，采用多条分析法对其进行了一系列非线性时程分析，确定了可作为样本集的地震需求烈度。在贝叶斯回归框架下，分别针对单变量和多变量情况，建立了基于上述两种方法的概率地震需求模型，并将拟合结果与传统的线性回归方法进行了比较分析。在考虑异方差的概率地震需求下，可以有效地刻画非常数方差或协方差的模式，并提供比同方差模型更好的校准预测区域。对异方差现象产生的原因和解决方法进行了深入的讨论。该分析程序可进一步嵌入地震易损性和风险评估中，从而为抗震决策提供更准确的依据。
英文摘要：
As an integral part of assessing the seismic performance of structures, the probabilistic seismic demand-intensity relationship has been widely studied. In this study, the phenomenon of heteroscedasticity in probabilistic seismic demand models was systematically investigated. A brief review of the definition, diagnosis, and conventional treatment of heteroscedasticity is presented herein, and based on that, two more generalized methods for both univariate and multivariate cases are proposed. For a typical four-span simply supported girder bridge, a series of nonlinear time history analyses were performed through multiple stripe analysis to determine its seismic demand-intensity that can be employed as a sample set. For both univariate and multivariate cases, probabilistic seismic demand models were developed based on the two aforementioned methods under the Bayesian regression framework, and the fitted results were compared and analyzed with the conventional models using linear regression approaches. In the presence of probabilistic seismic demand considering heteroscedasticity, the patterns of non-constant variance or covariance can be characterized effectively, and a better-calibrated prediction region than that of homoscedastic models can be provided. The causes of the heteroscedasticity phenomenon and subsequent solutions are thoroughly discussed. The analysis procedures can be further embedded in seismic fragility and risk assessment, thus providing a more accurate basis for aseismic decision-making.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
