| 湿陷性黄土场地强夯夯沉量的预测 |
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| 引用本文： | 张豫川,李彬. 湿陷性黄土场地强夯夯沉量的预测[J]. 路基工程, 2011, 0(1): 67-69,73 |
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| 作者姓名： | 张豫川 李彬 |
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| 作者单位： | 兰州大学西部灾害与环境力学教育部重点实验室, 兰州 730000 |
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| 摘 要： | 以湿陷性黄土地区的大量强夯工程实例为对象,分析、选择了影响夯沉量的五大主控因素作为BP神经网络模型的基本特征量,建立夯沉量与其之间的相关关系的BP网络模型,对夯沉量进行了预测分析。结果表明:BP神经网络模型能真实反映强夯夯沉量与主控因素之间的非线性关系,预测结果与实测值之间的相对误差小于10%,用该模型对强夯夯沉量进行预测是有效的。
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| 关 键 词： | 强夯 夯沉量 BP神经网络 湿陷性黄土 |
| 收稿时间： | 2019-11-12 |
Prediction of Dynamic Compaction Settlement in Collapsible Loess Area |
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| ZHANG Yu-chuan,LI Bin. Prediction of Dynamic Compaction Settlement in Collapsible Loess Area[J]. , 2011, 0(1): 67-69,73 |
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| Authors: | ZHANG Yu-chuan LI Bin |
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| Affiliation: | ZHANG Yu-chuan,LI Bin(Key Laboratory of Western Disaster and Environmental Mechanics of Ministryof Education,Lanzhou University,Lanzhou 730000,China) |
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| Abstract: | Taking a lot of dynamic compaction projects in collapsible loess area for example,five main factors influencing settlement are analyzed as the basic characteristic quantity of BP neural network model.BP network model of correlation between these factors and settlement is established to predict the settlement.It is indicated BP neural network model can truly reflect the non-linear relationship between the main factors and dynamic compaction settlement.The relative error between the predicted result and measu... |
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| Keywords: | dynamic compaction settlement BP neural network collapsible loess |
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