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场地条件校正的PGA放大系数简化估计方法

陈龙伟 吴晓阳 唐川

陈龙伟, 吴晓阳, 唐川. 场地条件校正的PGA放大系数简化估计方法[J]. 西南交通大学学报, 2022, 57(1): 173-181. doi: 10.3969/j.issn.0258-2724.20200508
引用本文: 陈龙伟, 吴晓阳, 唐川. 场地条件校正的PGA放大系数简化估计方法[J]. 西南交通大学学报, 2022, 57(1): 173-181. doi: 10.3969/j.issn.0258-2724.20200508
CHEN Longwei, WU Xiaoyang, TANG Chuan. Simplified Prediction Method for PGA Amplification Factors Corrected by Site Conditions[J]. Journal of Southwest Jiaotong University, 2022, 57(1): 173-181. doi: 10.3969/j.issn.0258-2724.20200508
Citation: CHEN Longwei, WU Xiaoyang, TANG Chuan. Simplified Prediction Method for PGA Amplification Factors Corrected by Site Conditions[J]. Journal of Southwest Jiaotong University, 2022, 57(1): 173-181. doi: 10.3969/j.issn.0258-2724.20200508

场地条件校正的PGA放大系数简化估计方法

doi: 10.3969/j.issn.0258-2724.20200508
基金项目: 国家重点研发计划(2018YFC1504004)
详细信息
    作者简介:

    陈龙伟(1983—),男,研究员,博士,博士生导师,研究方向为岩土地震工程、土动力学、工程抗震等,E-mail:chenlw@iem.ac.cn

  • 中图分类号: P315.9

Simplified Prediction Method for PGA Amplification Factors Corrected by Site Conditions

  • 摘要:

    地表峰值加速度(peak ground-motion acceleration,PGA)是直观反映地震动强度的一个物理量,概念清晰且工程应用方便. 场地条件校正的PGA及其校正方法是特定工程抗震设计需要解决的问题. 为此,选取日本具有地表和井下记录KiK-net台网的32个台站及地震数据,通过对实测地震数据分析,提出了一种场地校正的PGA放大系数(fPGA)的简化估计方法. 该方法通过数据回归给出fPGA概率密度函数参数与场地特征参数线性组合之间的线性和二次函数形式的拟合公式;并采用fPGA概率预测模型,提出了多超越概率水平地表PGA值的预测方法. 数据分析结果显示:场地fPGA具有随机不确定性,可以采用对数正态分布函数模拟,其概率密度函数的参数(均值和标准差)与单一场地特征参数相关性较小,但与场地特征参数的线性组合相关性较大. 模型预测值与实测数据吻合较好,验证了简化估计方法的可行性.

     

  • 图 1  选取台站场地特征参数的统计

    Figure 1.  Statistic histograms of site characteristic parameters

    图 2  IWTH20台站实测fPGAAPGAR的分布

    Figure 2.  Distribution of fPGA from IWTH20 with respect to APGAR

    图 3  拟合系数与场地特征参数的分布

    Figure 3.  Scattering of regressive coefficients with respect to site characteristic parameters

    图 4  拟合系数和Z(Vs30, D, T)线性拟合曲线与实测数据对比

    Figure 4.  Regressive coefficients linearly predicted by Z(Vs30, D, T) compared with real data

    图 5  拟合系数和Z(Vs30, D, T)二次函数拟合曲线与实测数据对比

    Figure 5.  Regressive coefficients quadratically predicted by Z(Vs30, D, T) compared with real data

    图 6  拟合系数和Z(Vse, D, T)线性拟合曲线与实测数据对比

    Figure 6.  Regressive coefficients linearly predicted by Z(Vse, D, T) compared with real data

    图 7  拟合系数和Z(Vse, D, T)二次函数拟合曲线与实测数据对比

    Figure 7.  Regressive coefficients quadratically predicted by Z(Vse, D, T) comparing with data

    图 8  多概率水平下地表PGA预测值与实测数据对比(中间变量Z = C1Vs30 + C2D + C3T

    Figure 8.  Prediction of surface PGA under different probability levels compared to observed data (Z = C1Vs30 + C2D + C3T)

    图 9  多概率水平下地表PGA预测值与实测数据对比(中间变量Z = C1Vse + C2D + C3T

    Figure 9.  Prediction of surface PGA under different probability levels compared to observed data (Z = C1Vse + C2D + C3T)

    表  1  所选Kik-net台站场地特征参数

    Table  1.   Site characteristic parameters from KiK-net

    台站Vs30/(m•s−1)Vse/( m•s−1)D/mT/s台站Vs30/(m•s−1)Vse/( m•s−1)D/mT/s
    AOMH17 378.4 196.6 8 0.163 IWTH26 371.1 228.2 10 0.175
    FKSH09 584.6 244.2 10 0.164 IWTH27 670.3 150.0 4 0.107
    FKSH12 448.5 357.1 22 0.244 KMMH02 576.7 218.4 6 0.110
    FKSH19 338.1 255.0 20 0.314 KMMH16 279.7 229.2 41 0.533
    IBRH11 242.5 197.1 30 0.495 KSRH03 249.8 213.2 34 0.523
    IBRH13 335.4 288.0 24 0.318 KSRH10 212.9 185.9 36 0.644
    IBRH14 829.1 180.0 2 0.044 MYGH04 849.8 220.0 4 0.073
    IBRH16 626.1 205.9 5 0.097 MYGH05 305.3 120.0 2 0.067
    IBRH18 558.6 432.0 15 0.139 MYGH06 593.1 200.0 2 0.040
    IWTH04 455.9 314.3 15 0.191 MYGH09 358.2 315.8 38 0.400
    IWTH05 429.2 276.9 9 0.130 MYGH10 347.5 329.6 34 0.386
    IWTH18 891.6 180.0 2 0.044 MYGH11 859.2 210.0 3 0.057
    IWTH20 288.8 283.4 46 0.629 TCGH07 419.5 343.8 22 0.253
    IWTH21 521.1 326.5 12 0.168 TCGH12 343.7 305.1 50 0.523
    IWTH23 922.9 370.0 4 0.043 TCGH14 849.0 275.0 4 0.058
    IWTH24 486.4 360.0 10 0.111 TKCH08 353.2 312.0 36 0.390
    下载: 导出CSV

    表  2  选取台站拟合系数

    Table  2.   Regressive parameters for selected station sites

    台站a1b1a2b2台站a1b1a2b2
    AOMH17−0.0981.724−0.2660.741IWTH26−0.1362.085−0.2941.163
    FKSH09−0.1191.994−0.2150.945IWTH27−0.0812.134−0.1891.005
    FKSH12−0.1832.654−0.3311.855KMMH02−0.0811.617−0.0540.473
    FKSH19−0.1142.266−0.1110.740KMMH16−0.0921.924−0.1270.854
    IBRH11−0.1172.332−0.1120.966KSRH03−0.1551.754−0.1770.567
    IBRH13−0.0982.311−0.1381.118KSRH10−0.1392.036−0.1790.816
    IBRH14−0.1122.128−0.3461.427MYGH04−0.1112.023−0.1580.943
    IBRH16−0.1452.421−0.2091.386MYGH05−0.0551.149−0.204−0.094
    IBRH18−0.1032.211−0.1541.253MYGH06−0.0220.592−0.068−0.846
    IWTH04−0.1131.980−0.2010.724MYGH09−0.0521.149−0.181−0.046
    IWTH05−0.1342.212−0.2111.026MYGH10−0.0441.614−0.1020.157
    IWTH18−0.1152.168−0.3011.314MYGH11−0.1002.002−0.1620.859
    IWTH20−0.0300.831−0.152−0.453TCGH07−0.1452.200−0.2570.951
    IWTH21−0.1202.133−0.1140.860TCGH12−0.0811.402−0.1320.089
    IWTH23−0.0901.884−0.2010.815TCGH14−0.2512.596−0.6361.947
    IWTH24−0.0270.954−0.075−0.365TKCH08−0.0771.927−0.1650.941
    下载: 导出CSV

    表  3  Vs30DT参数组合Z情况下待定系数值

    Table  3.   Regressive coefficients corresponding to Z in combination of Vs30, D and T

    函数类型待定系数拟合系数
    a1b1a2b2
    线性函数 C1 0.001 −0.002 −0.008 −0.003
    C2 −0.044 0.045 0.051 0.044
    C3 3.905 −3.946 −3.946 −3.935
    C4 −0.061 1.456 −0.096 0.049
    C5 −0.060 −0.479 0.025 −0.483
    二次函数 C1 0.001 −0.003 −0.002 −0.003
    C2 0.031 −0.062 −0.003 −0.046
    C3 0.409 −3.445 −0.788 −3.235
    C4 0.038 −0.589 −0.550 −2.427
    C5 −0.265 −1.603 −0.707 −2.311
    C6 0.109 −0.228 −0.325 −0.381
    下载: 导出CSV

    表  4  VseDT参数组合Z情况下待定系数值

    Table  4.   Regressive coefficients corresponding to Z in combination of Vse, D and T

    函数类型待定系数拟合系数
    a1b1a2b2
    线性函数 C1 −0.002 −0.003 −0.001 −0.004
    C2 0.057 0.060 −0.017 0.071
    C3 −3.932 −3.884 −3.922 −3.899
    C4 −0.059 1.377 −0.243 0.393
    C5 0.081 −0.764 −0.032 −0.587
    二次函数 C1 0.004 0.006 −0.001 0.006
    C2 0.007 0.043 0.028 0.021
    C3 1.020 2.734 −3.786 2.767
    C4 0.094 0.651 −0.305 −0.614
    C5 −0.347 0.990 −0.306 1.424
    C6 0.134 −0.172 −0.143 −0.306
    下载: 导出CSV

    表  5  不同方法中拟合系数的残差平方和

    Table  5.   Sum of squared residuals of regressive coefficients in different methods

    中间变量函数类型拟合系数残差平方和
    a1b1a2b2
    Z(Vs30, D, T) 线性函数 0.060 7.042 0.312 10.459
    二次函数 0.054 4.364 0.296 7.734
    Z(Vse, D, T) 线性函数 0.060 6.857 0.334 10.937
    二次函数 0.051 5.672 0.328 9.708
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-08-04
  • 修回日期:  2020-11-25
  • 网络出版日期:  2020-12-25
  • 刊出日期:  2020-12-25

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