地震研究

基于成都市地震安评报告中的场地钻孔剪切波速资料,定性探讨了成都地区Ⅱ类场地常见的10类土的剪切波速与埋深的关系; 将拟合优度作为模型的综合评价指标,推荐给出针对不同类型土层的拟合函数模型。基于某工程的钻孔数据,利用推荐的数学回归模型和遗传神经网络模型,与其它研究模型计算得到的剪切波速预测值及实测值进行对比分析。结果表明:成都地区常见土类的剪切波速与埋深存在一定的相关性,且随埋藏深度的不断增大,离散程度也随之增大; 推荐的两种模型得到的剪切波速值更接近于实测值,预测精度均较高。

Using the shear wave velocity from the borehole data from the Report of Chengdu Seismic Safety Evaluation,we qualitatively study the relationship between the shear wave velocity and soil depth of ten common types of the soil in Chengdu region.Then,taking the goodness of fit as a comprehensive evaluation index,we set up fitting function models corresponding to various types of soil layers.Using the borehole data from a drainage project of a tunnel in Chengdu,we compared the shear wave velocty evaluated by the mathematical model we set up,and the shear wave velocity evaluated by GA-BP model,and the ones by other models.We find that the shear wave velocity is related with the soil depth,and the dispersion increases when the depth increases.The model recommended in this paper has higher appraisal precision.

引言
1 研究方法
2 数据整理及剪切波速分布
3 剪切波速与埋深关系
4 模型验证
5 结论

图1 基于遗传算法的BP神经网络算法流程<br/>Fig.1 The flow chart of BP neural network based on the genetic algorithm

图1 基于遗传算法的BP神经网络算法流程
Fig.1 The flow chart of BP neural network based on the genetic algorithm

表1 成都地区土的类型划分和剪切波速范围<br/>Tab.1 Classification of the soil and corresponding shear wave velocities in Chengdu region

表1 成都地区土的类型划分和剪切波速范围
Tab.1 Classification of the soil and corresponding shear wave velocities in Chengdu region

图2 部分钻孔柱状图<br/>Fig.2 Part of the bore histograms[HT]

图2 部分钻孔柱状图
Fig.2 Part of the bore histograms[HT]

表2 成都地区Ⅱ类场地各类土体的基本信息<br/>Tab.2 Basic information of different soil on Class Ⅱ Site in Chengdu region

表2 成都地区Ⅱ类场地各类土体的基本信息
Tab.2 Basic information of different soil on Class Ⅱ Site in Chengdu region

图3 成都地区剪切波速在不同埋深范围的频次分布直方图<br/>Fig.3 Histograms of the shear wave velocity at different depths in Chengdu area

图3 成都地区剪切波速在不同埋深范围的频次分布直方图
Fig.3 Histograms of the shear wave velocity at different depths in Chengdu area

图4 4种不同密实度的卵石在不同剪切波速的频次分布直方图和正态分布<br/>Fig.4 Histograms and normal distribution of 4 types pebble soil in different shear wave velocities

图4 4种不同密实度的卵石在不同剪切波速的频次分布直方图和正态分布
Fig.4 Histograms and normal distribution of 4 types pebble soil in different shear wave velocities

表3 成都地区内Ⅱ类场地常规土类回归模型的拟合参数和拟合优度<br/>Tab.3 Fitting parameters and goodness of conventional soil regression model for type II site in Chengdu region

表3 成都地区内Ⅱ类场地常规土类回归模型的拟合参数和拟合优度
Tab.3 Fitting parameters and goodness of conventional soil regression model for type II site in Chengdu region

图5 成都地区各类土体剪切波速与埋深关系的散点图<br/>Fig.5 Shear wave velocity of soil versus the depth in Chengdu region

图5 成都地区各类土体剪切波速与埋深关系的散点图
Fig.5 Shear wave velocity of soil versus the depth in Chengdu region

表4 基于本文数学回归模型、遗传神经网络模型及其它模型按土体埋深所得剪切波速预测值(Ⅱ类场地)<br/>Tab.4 Predicted shear wave velocities according to the soil depth based on the model of genetic neural network,the mathematical regression model by the author,and the model by Liu

表4 基于本文数学回归模型、遗传神经网络模型及其它模型按土体埋深所得剪切波速预测值(Ⅱ类场地)
Tab.4 Predicted shear wave velocities according to the soil depth based on the model of genetic neural network,the mathematical regression model by the author,and the model by Liu

图6 遗传代数同误差值(a)、实验误差平方和随训练次数(b)变化曲线及遗传神经网络模型输出回归分析(c)<br/>Fig.6 Curve of variation of genetic algebra and error value(a),sum of squares of experimental errors vs training times(b),and the recursive analysisof GA-BP network output(c)

图6 遗传代数同误差值(a)、实验误差平方和随训练次数(b)变化曲线及遗传神经网络模型输出回归分析(c)
Fig.6 Curve of variation of genetic algebra and error value(a),sum of squares of experimental errors vs training times(b),and the recursive analysisof GA-BP network output(c)

图7 不同方法的输出结果对比<br/>Fig.7 Shear wave velocities obtained by different models

图7 不同方法的输出结果对比
Fig.7 Shear wave velocities obtained by different models

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备注

引言

1 研究方法

2 数据整理及剪切波速分布

3 剪切波速与埋深关系

4 模型验证

5 结论

期刊信息