| 基于径向基函数神经网络的地震液化侧移预测 |
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| 引用本文： | 范珂显,李恒,张祎.基于径向基函数神经网络的地震液化侧移预测[J].大地测量与地球动力学,2021,41(12):1271-1275. |
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| 作者姓名： | 范珂显 李恒 张祎 |
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| 作者单位： | 中国地震局地震大地测量重点实验室,武汉市洪山侧路40号,430071;中国地震局地震大地测量重点实验室,武汉市洪山侧路40号,430071;武汉地震工程研究院有限公司,武汉市洪山侧路40号,430071 |
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| 摘 要： | 在已有的地震液化侧移数据库中增加累积绝对速度（CAV5）这一地震参数，以考虑震源机制对液化侧移的影响。然后采用径向基函数神经网络（RBFNN）方法建立地震液化侧移预测模型，并与其他模型进行对比分析。结果表明，本文模型预测精确度最高；CAV5在液化侧移预测方面可以代替震级、震中距2项参数；所有参数中，震级、震中距、可液化土层厚度敏感性较高，对液化侧移影响程度较大。
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| 关 键 词： | 地震震害 液化侧移 径向基函数神经网络 敏感性分析 影响参数 |
Evaluation of Earthquake Liquefaction-Induced Lateral Spread Based on RBFNN |
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| FAN Kexian,LI Heng,ZHANG Yi.Evaluation of Earthquake Liquefaction-Induced Lateral Spread Based on RBFNN[J].Journal of Geodesy and Geodynamics,2021,41(12):1271-1275. |
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| Authors: | FAN Kexian LI Heng ZHANG Yi |
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| Abstract: | In this study, we add the cumulative absolute velocity (CAV5) to the existing seismic liquefaction-induced lateral spread database to consider the effect of focal mechanism on liquefaction-induced lateral spread. Then, we use the radial basis function neural network (RBFNN) method to establish the liquefaction-induced lateral spread prediction model of earthquake liquefaction. The results show that our model has higher prediction accuracy than other models; CAV5 can replace the magnitude and epicentral distance in the prediction of liquefaction-induced lateral spread. The magnitude, epicentral distance and liquefiable soil layer thickness of all parameters are more sensitive and have a greater impact on liquefaction-induced lateral spread. |
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| Keywords: | earthquake damage liquefaction-induced lateral spread RBFNN sensitivity analysis affecting parameters |
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