| 预制桩承载力时效的人工神经网络预测 |
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| 引用本文： | 王伟,卢廷浩,宰金珉. 预制桩承载力时效的人工神经网络预测[J]. 水运工程, 2004, 0(11): 9-12 |
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| 作者姓名： | 王伟 卢廷浩 宰金珉 |
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| 作者单位： | 1. 河海大学岩土工程研究所,江苏,南京,210098
2. 南京工业大学岩土工程研究所,江苏,南京,210009 |
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| 基金项目： | 江苏省应用基础基金(NO.BJ97051)。 |
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| 摘 要： | 分析预制桩单桩承载力时间效应的机理，说明其主要与桩长、桩截面积、土体摩擦角、渗透系数、弹性模量、休止期6个参数有关，建立了考虑时间效应的预测单桩承载力的误差反馈型神经网络模型。该模型充分考虑了桩周土固结对桩承载力的影响，输入层神经元为以上6个参数，物理意义明确且容易确定；训练模式采用共轭梯度法。对34根桩的计算表明该模型预测结果与实测值吻合，说明其是科学有效的。
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| 关 键 词： | 人工神经网络 预制桩 极限承载力 时间效应 |
| 文章编号： | 1002-4972(2004)11-0009-04 |
Prediction of Time-dependent Bearing Capacity of Driven Piles by Using Artificial Neural Networks |
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| WANG Wei,LU Ting-hao,ZAI Jin-min. Prediction of Time-dependent Bearing Capacity of Driven Piles by Using Artificial Neural Networks[J]. Port & Waterway Engineering, 2004, 0(11): 9-12 |
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| Authors: | WANG Wei LU Ting-hao ZAI Jin-min |
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| Affiliation: | WANG Wei1,LU Ting-hao1,ZAI Jin-min2 |
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| Abstract: | Time effect on ultimate bearing capacity of driven piles is analyzed. Conclusion that the effect mainly depends on pile length, area of pile section, soil friction angle, soil consolidation coefficient, soil elastic module and time after pile installation is obtained. Considering time effect and soil consolidation, model of artificial neural networks to predict the bearing capacity is established. Input layer consists six parameters discussed above, and it is easy to get them. Conjugate gradient method is adopted to train the net. Based on calculation of 34 practical piles, results of the model are found to be in good agreement with field tests, and the efficiency of the present model is proved. |
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| Keywords: | artificial neural networks driven pile ultimate bearing capacity time effect |
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