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A Modified Algorithm to identify the strongest velocity pulse in three orthogonal components of ground motions

机译:一种修改的算法,用于识别地面运动三个正交分量中最强的速度脉冲

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摘要

An algorithm for the identification of the strongest velocity pulse is proposed based on wavelet analysis, which considers three orthogonal components of the ground motion. According to the NGA-West2 database, 6288 strong motion records were analyzed, and further, the classification results were compared with those obtained from the existing identification method of Shahi and Baker (2014) [1]. Among the results, our algorithm classified more records as pulse-like ground motion and has expanded the pulse identifications. In other words, the proposed algorithm can reflect the three orthogonal characteristics of the original ground motion records. Besides, the relationships of pulse amplitude, period with magnitude and fault distance were analyzed. Based on the analysis of near-fault ground motions containing velocity pulses, the following conclusions can be made: (1) The peak of the strongest velocity pulse gradually reduces with the increase of the fault distance, while gradually rises with the increase of the magnitude. Comparing with the fault normal or fault parallel orientation component, the strongest velocity pulse component has larger energy and smaller peak velocity. (2) For the relationship between the velocity pulse period and magnitude, results show that when Mw 7, the predicted value of our model is larger than that of the Shahi and Baker (2014) [1] model; however when Mw = 7, the prediction results from these two models are fairly close. The difference between the two models decreases as magnitude increases. That is because the strongest velocity pulses occupied more in the horizontal direction under a large magnitude.
机译:基于小波分析,提出了一种用于识别最强速度脉冲的算法,其考虑了地面运动的三个正交分量。根据NGA-WIST2数据库,分析了6288个强大的运动记录,进一步,将分类结果与来自Shahi和Baker(2014)的现有识别方法中获得的那些进行了比较[1]。在结果中,我们的算法将更多的记录分为脉冲状地面,并且扩展了脉冲识别。换句话说,所提出的算法可以反映原始地面运动记录的三个正交特性。此外,分析了脉冲幅度,周期的关系,幅度和故障距离。基于含有速度脉冲的近断层地运动的分析,可以进行以下结论:(1)最强速度脉冲的峰值随着故障距离的增加而逐渐减少,同时随着幅度的增加而逐渐上升。与故障正常或故障并联取向分量相比,最强的速度脉冲分量具有较大的能量和更小的峰值速度。 (2)对于速度脉冲周期与幅度之间的关系,结果表明当MW& 7,我们模型的预测值大于Shahi和Baker(2014)[1]模型;然而,当MW& = 7时,这两个模型的预测结果相当接近。随着幅度的增加,两种模型之间的差异降低。这是因为在大幅度下,最强的速度脉冲在水平方向上占用。

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