| BP神经网络预测嵌岩桩承载力 |
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| 引用本文： | 王勇刚,董文蔚.BP神经网络预测嵌岩桩承载力[J].岩土工程技术,2005,19(5):227-232. |
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| 作者姓名： | 王勇刚 董文蔚 |
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| 作者单位： | 1. 铁道第四勘察设计院,湖北,武汉,430063
2. 上海谆星商务咨询有限公司,上海,200010 |
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| 摘 要： | 确定嵌岩桩承载力的最可靠最直接的方法是静载试验,但是由于嵌岩桩承载力大,静载试验耗工费时,并且很难做到破坏,因此工程界希望能在不影响结果精度的前提下尽可能少做静载试验。利用以往的嵌岩桩静载试验资料,在BP神经网络理论的基础上,运用Matlab中的神经网络工具箱进行编程分析,总结出嵌岩桩的各种可控参数对其承载能力的影响,从而确定最终比较合理的嵌岩桩的设计参数。对比分析前人的研究成果,得出的结论具有一定的实用性。
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| 关 键 词： | 嵌岩桩 极限承载力 BP神经网络 静载试验 |
| 文章编号： | 1007-2993(2005)05-0227-06 |
| 修稿时间： | 2005年7月12日 |
Prediction of Bear Capacity of Rock-socketed Pile by BP Neural Network |
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| Wang Yonggang,Dong Wenwei.Prediction of Bear Capacity of Rock-socketed Pile by BP Neural Network[J].Geotechnical Engineering Technique,2005,19(5):227-232. |
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| Authors: | Wang Yonggang Dong Wenwei |
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| Institution: | Wang Yonggang~1 Dong Wenwei~2 |
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| Abstract: | The most direct and credible method to calculate the bearing capacity of the rock-socketed pile is the static load test.But because the bearing capacity of the rock-socketed pile is very large and the expense of the experiment on rock-socketed pile is high and the experiment is very hard to go to the destroy point,so it is necessary to cut down the number of the static load test but not to reduce the precision of the result in the scope of the engineering.According to the former test data,and on the basis of the BP neural network,and using the toolbox of the neural network in the Matlab,the influence to the bearing capacity of the rock?socketed pile by several controllable element is concluded.Then the reasonable design parameters of the rock?socketed pile are confirmed. The analysis shows the practical function of BP neural network method in some degree. |
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