| 嵌岩桩竖向承载力影响因素的两种方法分析 |
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| 引用本文： | 王昆明,徐艳,尹大凯. 嵌岩桩竖向承载力影响因素的两种方法分析[J]. 工业建筑, 2011, 0(Z1): 456-458,685 |
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| 作者姓名： | 王昆明 徐艳 尹大凯 |
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| 作者单位： | 山东同圆设计集团有限公司;济南四建集团有限责任公司; |
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| 摘 要： | 为了更好地分析嵌岩桩单桩竖向极限承载力的影响因素,提出并分析了两种研究方法。分析结果表明:自组织特征映射神经网络可以客观的找出最主要的影响因素,但结果受参数量化影响大;遗传BP神经网络可以建立起多影响因素与极限承载力的关系,特别是在研究地域范围内桩基承载力方面优势明显,但需要大量的学习样本,且预测范围受学习范围限制。
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| 关 键 词： | 嵌岩桩 自组织特征映射网络 遗传BP神经网络 单桩竖向极限承载力影响因素 |
THE ANALYSIS OF TWO METHODS USED TO STUDY INFLUENCE FACTORS OF VERTICAL BEARING CAPACITY OFSINGLE ROCK-SOCKETED PILE |
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| Wang Kunming Xu Yan Yin Dakai. THE ANALYSIS OF TWO METHODS USED TO STUDY INFLUENCE FACTORS OF VERTICAL BEARING CAPACITY OFSINGLE ROCK-SOCKETED PILE[J]. Industrial Construction, 2011, 0(Z1): 456-458,685 |
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| Authors: | Wang Kunming Xu Yan Yin Dakai |
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| Affiliation: | Wang Kunming1 Xu Yan1 Yin Dakai2(1.Shandong Tongyuan Design Group Co.Ltd,Jinan 250101,China,2.Jinan Sijian Group Construction Co.Ltd,Jinan 250031,China) |
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| Abstract: | Two methods used to study influence factor of vertical ultimate bearing capacity of single rock-socketed pile were analyzed.With the self-organizing feature map,the most importan t influence factor could be found out.But this method could be affected easily by quantization parameter.Based on the genetic algorithm and back propagation neural networks prediction model,the relationship between the vertical ultimate bearing capacity of single rock-socketed pile and influence factors was established.But this mod... |
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| Keywords: | rock-socketed pile self-organizing feature map the genetic algorithm and back propagation neural networks influence factors of vertical ultimate bearing capacity of single piles |
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