| 基于神经网络的大连东北海岸带碎石土地基强夯加固研究 |
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| 引用本文： | 邓浩,张延军,单坤,倪金,岳高凡.基于神经网络的大连东北海岸带碎石土地基强夯加固研究[J].世界地质,2020,39(1):121-126. |
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| 作者姓名： | 邓浩 张延军 单坤 倪金 岳高凡 |
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| 作者单位： | 吉林大学建设工程学院,长春,130026;中国地质调查局沈阳地质调查中心,沈阳,110000;中国地质科学院水文地质环境地质研究所,石家庄,050060 |
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| 基金项目： | 国家重点研发计划项目(2018YFB150180305);国家自然科学基金项目(41772238);大连海岸带陆海统筹综合地质调查项目(DD20189504)联合资助。 |
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| 摘 要： | 以大连某实际工程作为研究场地,室内试验与原位测试所得碎石土地基物理力学参数与实测所得强夯处理沉降量作为样本,通过BP神经网络对样本的训练、学习,建立地基土力学参数与强夯处理的沉降量之间的映射关系,利用所得映射关系对场地实测的沉降量进行物理力学参数的反演分析。结果表明:经过训练的神经网络模型可快速得出所需参数,利用flac3d以反演所得参数进行计算,模拟沉降量与实测沉降量的误差为4.87%,在可接受的范围之内;基于神经网络的位移反分析方法可以省去繁琐的测试工作,但该方法的实现需要有充足的样本数据作为支撑。
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| 关 键 词： | 强夯加固 沉降 神经网络 位移反分析 |
Study on dynamic compaction of gravelly soil foundation in northeast coast of Dalian based on neural network |
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| DENG Hao,ZHANG Yan-jun,SHAN Kun,NI Jin,YUE Gao-fan.Study on dynamic compaction of gravelly soil foundation in northeast coast of Dalian based on neural network[J].World Geology,2020,39(1):121-126. |
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| Authors: | DENG Hao ZHANG Yan-jun SHAN Kun NI Jin YUE Gao-fan |
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| Institution: | (College of Construction Engineering,Jilin University,Changchun 130026,China;Shenyang Geological Survey Center,China Geological Survey,Shenyang 110000,China;Institute of Hydrogeology and Environmental Geology,Chinese Academy of Geological Sciences,Shijiazhuang 050060,China) |
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| Abstract: | Taking a practical project in Dalian as the research site,the sample data were obtained from the laboratory test and in-situ test of the physical and mechanical parameters of gravelly soil foundation and the measured settlement of dynamic compaction.By training and learning the samples using BP neural network,the mapping relationship between the mechanical parameters of foundation soil and the settlement amount from dynamic compaction was established,which was then used for the inversion analysis of physical and mechanical parameters of measured settlement.The results showed that the trained neural network model can quickly output required parameters.Flac3d was used to calculate the parameters obtained by inversion.The difference between simulated settlement and measured settlement was 4.87%,which is within the acceptable limit.The displacement inverse analysis based on neural network can save tedious testing workflow,but the implementation of this method needs the support of sufficient sample data. |
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| Keywords: | dynamic compaction settlement neural network displacement inverse analysis |
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