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Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods

机译：基于ANN方法的土压平衡（EPB）盾构隧道施工引起的最大表面沉降预测

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摘要

In order to determine the appropriate model for predicting the maximum surface settlement caused by EPB shield tunneling, three artificial neural network (ANN) methods, back-propagation (BP) neural network, the radial basis function (RBF) neural network, and the general regression neural network (GRNN), were employed and the results were compared. The nonlinear relationship between maximum ground surface settlements and geometry, geological conditions, and shield operation parameters were considered in the ANN models. A total number of 200 data sets obtained from the Changsha metro line 4 project were used to train and validate the ANN models. A modified index that defines the physical significance of the input parameters was proposed to quantify the geological parameters, which improves the prediction accuracy of ANN models. Based on the analysis, the GRNN model was found to outperform the BP and RBF neural networks in terms of accuracy and computational time. Analysis results also indicated that strong correlations were established between the predicted and measured settlements in GRNN model with MAE = 1.10, and RMSE = 1.35, respectively. Error analysis revealed that it is necessary to update datasets during EPB shield tunneling, though the database is huge. (C) 2019 Production and hosting by Elsevier B.V. on behalf of The Japanese Geotechnical Society.
机译：为了确定用于预测由EPB盾构隧道引起的最大表面沉降的适当模型，使用了三种人工神经网络（ANN）方法，反向传播（BP）神经网络，径向基函数（RBF）神经网络和通用使用回归神经网络（GRNN）并比较结果。在ANN模型中考虑了最大地面沉降量与几何形状，地质条件和盾构运行参数之间的非线性关系。从长沙地铁四号线项目获得的总共200个数据集用于训练和验证ANN模型。提出了定义输入参数的物理意义的改进指标来量化地质参数，从而提高了人工神经网络模型的预测精度。在分析的基础上，发现GRNN模型在准确性和计算时间方面均优于BP和RBF神经网络。分析结果还表明，在GRNN模型中，MAE = 1.10和RMSE = 1.35的沉降预测值与实测值之间建立了很强的相关性。错误分析表明，尽管数据库很大，但在EPB盾构隧道化过程中有必要更新数据集。 （C）2019年由Elsevier B.V.代表日本岩土工程学会制作和主持。

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  • 来源
    《Soils and foundations》 |2019年第2期|284-295|共12页
  • 作者

    Chen Ren-Peng; Zhang Pin; Kang Xin; Zhong Zhi-Quan; Liu Yuan; Wu Huai-Na;

  • 作者单位

    Hunan Univ, Key Lab Bldg Safety & Energy Efficiency, Minist Educ, Changsha 410082, Hunan, Peoples R China|Hunan Univ, Natl Joint Res Ctr Bldg Safety & Environm, Changsha 410082, Hunan, Peoples R China|Hunan Univ, Coll Civil Engn, Changsha 410082, Hunan, Peoples R China;

    Hunan Univ, Coll Civil Engn, Changsha 410082, Hunan, Peoples R China;

    Hunan Univ, Key Lab Bldg Safety & Energy Efficiency, Minist Educ, Changsha 410082, Hunan, Peoples R China|Hunan Univ, Natl Joint Res Ctr Bldg Safety & Environm, Changsha 410082, Hunan, Peoples R China|Hunan Univ, Coll Civil Engn, Changsha 410082, Hunan, Peoples R China;

    China Construct Fifth Engn Div Co Ltd, Changsha 410082, Hunan, Peoples R China;

    Hunan Univ, Coll Civil Engn, Changsha 410082, Hunan, Peoples R China;

    Hunan Univ, Key Lab Bldg Safety & Energy Efficiency, Minist Educ, Changsha 410082, Hunan, Peoples R China|Hunan Univ, Natl Joint Res Ctr Bldg Safety & Environm, Changsha 410082, Hunan, Peoples R China|Hunan Univ, Coll Civil Engn, Changsha 410082, Hunan, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Neural network; EPB shield; Tunnel; Settlement prediction; Field instrumentation;

    机译：神经网络;EPB盾;隧道;结算预测;现场仪器;

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