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Predicting lateral displacement caused by seismic liquefaction and performing parametric sensitivity analysis: Considering cumulative absolute velocity and fine content

机译:预测地震液化和执行参数敏感性分析引起的横向位移:考虑累积绝对速度和精细含量

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摘要

Lateral displacement due to liquefaction (D-H) is the most destructive effect of earthquakes in saturated loose or semi-loose sandy soil. Among all earthquake parameters, the standardized cumulative absolute velocity (CAV(5)) exhibits the largest correlation with increasing pore water pressure and liquefaction. Furthermore, the complex effect of fine content (FC) at different values has been studied and demonstrated. Nevertheless, these two contexts have not been entered into empirical and semi-empirical models to predict D-H This study bridges this gap by adding CAV(5) to the data set and developing two artificial neural network (ANN) models. The first model is based on the entire range of the parameters, whereas the second model is based on the samples with FC values that are less than the 28% critical value. The results demonstrate the higher accuracy of the second model that is developed even with less data. Additionally, according to the uncertainties in the geotechnical and earthquake parameters, sensitivity analysis was performed via Monte Carlo simulation (MCS) using the second developed ANN model that exhibited higher accuracy. The results demonstrated the significant influence of the uncertainties of earthquake parameters on predicting D-H.
机译:由于液化(D-H)引起的横向位移是地震在饱和松散或半松散的沙质土壤中的最具破坏性效果。在所有地震参数中,标准化的累积绝对速度(CAV(5))表现出与增加孔隙水压力和液化的最大相关性。此外,已经研究并证明了不同值下细含量(Fc)的复杂效果。然而,这两个上下文尚未进入经验和半经验模型以预测D-H这项研究通过将CAV(5)添加到数据集并开发两个人工神经网络(ANN)模型来桥接该差距。第一模型基于参数的整个范围,而第二种模型基于具有小于28%临界值的FC值的样本。结果表明,即使数据较少的数据也是更高的第二种模型的准确性。另外,根据岩土和地震参数的不确定性,使用蒙特卡罗模拟(MCS)进行敏感性分析,使用第二开发的ANN模型表现出更高的精度。结果表明,地震参数的不确定性对预测D-H的显着影响。

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