| 基于并联型灰色神经网络模型的隧道沉降量预测方法探讨 |
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| 引用本文： | 曾贤敏,黄腾,谢朋朋.基于并联型灰色神经网络模型的隧道沉降量预测方法探讨[J].勘察科学技术,2016(5). |
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| 作者姓名： | 曾贤敏 黄腾 谢朋朋 |
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| 作者单位： | 河海大学地球科学与工程学院 南京市210098 |
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| 基金项目： | 中央高校基本科研业务经费(2012B01714)，软土地铁隧道运营期沉降成因分析、沉降预测及稳定性评判方法研究 |
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| 摘 要： | 灰色预测方法和人工神经网络,在建筑物变形预测中有各自的优势和不足.为了提高预测精度,该文结合灰色GM(1,1)模型和BP神经网络模型的特点,构造并联型灰色神经网络模型(PGNN)对南京地铁隧道某监测点的沉降量进行预测.结果显示,PGNN的预测精度明显高于单一的灰色GM(1,1)模型和BP神经网络模型,证明了PGNN组合方法在地铁隧道沉降量预测中的有效性.
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| 关 键 词： | 隧道沉降 灰色模型 神经网络 预测方法 |
Discussion on Tunnel Settlement Prediction Method Based on Parallel Grey Neural Network Model |
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| Abstract: | Grey forecast method and artificial neural network have their own advantages and disadvantages in building deformation prediction.In order to improve the prediction accuracy,in this paper,combined with the characteristics of GM (1,1) and BP neural network model,the parallel grey neural network model(PGNN) is constructed and applied to forecast the settlement of a tunnel monitoring point of Nanjing metro.The results show that the prediction accuracy of PGNN is significantly higher than that of GM (1,1) and BP neural network model.It proves the effectiveness of PGNN in the metro tunnel settlement prediction. |
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| Keywords: | tunnel settlement grey model neural network prediction method |
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