唐川, 陈龙伟. 场地校正的地表PGA放大系数概率模型研究[J]. 工程力学, 2020, 37(12): 99-113. DOI: 10.6052/j.issn.1000-4750.2020.01.0023
引用本文: 唐川, 陈龙伟. 场地校正的地表PGA放大系数概率模型研究[J]. 工程力学, 2020, 37(12): 99-113. DOI: 10.6052/j.issn.1000-4750.2020.01.0023
TANG Chuan, CHEN Long-wei. PROBABILITY MODELLING OF PGA AMPLIFICATION FACTORS CORRECTED BY SITE CONDITIONS[J]. Engineering Mechanics, 2020, 37(12): 99-113. DOI: 10.6052/j.issn.1000-4750.2020.01.0023
Citation: TANG Chuan, CHEN Long-wei. PROBABILITY MODELLING OF PGA AMPLIFICATION FACTORS CORRECTED BY SITE CONDITIONS[J]. Engineering Mechanics, 2020, 37(12): 99-113. DOI: 10.6052/j.issn.1000-4750.2020.01.0023

场地校正的地表PGA放大系数概率模型研究

PROBABILITY MODELLING OF PGA AMPLIFICATION FACTORS CORRECTED BY SITE CONDITIONS

  • 摘要: 地表峰值加速度PGA是抗震设计规范和地震预警系统中重要的参数指标,场地PGA估计是工程抗震的必要基础。该文选取日本具有地表和井下记录KiK-net台网的40个台站,研究场地PGA放大系数fPGA的概率分布特征,指出fPGA在给定地震动强度输入情况下基本服从对数正态分布,其概率分布参数(即均值和标准差)与场地土层特征相关;fPGA的概率分布参数与单独的场地工程参数Vs30Vs20以及场地覆盖层厚度D的相关性较小,但与这些特征参数的线性组合具有一定相关性;通过数据挖掘,建立fPGA概率分布参数与Vs30Vs20以及D线性组合之间的关联性;采用fPGA概率模型,可以给出多概率水平下地表PGA的预测,实测数据检验了提出的PGA概率预测模型的可靠性。通过该文建立的地表PGA概率预测方法,可为地震预警及烈度速报技术中,地表PGA概率预测的场地校正技术提供一种可行的途径。

     

    Abstract: Peak ground-motion acceleration (PGA) is a fundamental parameter in seismic design codes and in earthquake early warning systems. We collected the seismic data recorded by 40 strong-motion stations of the KiK-net seismic station array in Japan, and studied the statistic distribution of PGA amplification factor fPGA. It was demonstrated that the fPGA under a given seismic intensity input was basically log-normally distributed with its mean and standard deviation depending on the site conditions. Any individual site characteristic parameter, such as Vs30, Vs20 or soil thickness D, was poorly correlated with the statistic parameters, i.e., the mean and standard deviation, while a satisfactory correlation was obtained with respect to linear combinations of Vs30, Vs20 and D. By the regression of the data, the statistic parameters of fPGA were calculated according to a linear combination of site characteristic parameters to build the probability density function of the log-normal distribution model of fPGA. Following the fPGA probability model, the ground surface PGA corrected by specific site characteristic parameters could be predicted under different probability levels, and testified by seismic data. The probabilistic predictions of PGA meet the demands of risk analysis in engineering practice, paving the way for site-corrected surface PGA prediction in earthquake early warning and fast seismic intensity estimation.

     

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