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Modeling the relationship between ground surface settlements induced by shield tunneling and the operational and geological parameters based on the hybrid PCA/ANFIS method

机译:基于PCA / ANFIS混合方法的盾构隧道掘进地表沉降与运营和地质参数之间的关系建模

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This paper proposes a methodology that combines the Principal Component Analysis (PCA) with Adaptive Neuro Fuzzy based Inference System (ANFIS) to model the nonlinear relationship between ground surface settlements induced by an earth pressure balanced TBM and the operational and geological parameters. The study is based on data recorded during the excavation of contract 2 of the subway line B tunnel in Toulouse (France). Prior to modeling, principal components analysis (PCA) and agglomerative hierarchical clustering (AHC) are used to describe the interrelation pattern between the TBM parameters and geology profiles. At first, a model ANFIS based on 10 selected TBM operation parameters (geology conditions considered as homogenous) is developed and validated by drawing the settlement profiles for different reference points. Secondly, to take into account the effect of geology on the settlements, 5 parameters representing the thicknesses of categories of soil were added as input variables. Then, a model ANFIS using the significant principal components as inputs is developed and validated. The results indicate a high correlation between predicted and measured settlements despite the low amount of data used in the analysis. In addition, the model is able to predict Gaussian troughs for the representative groups identified by the AHC. The results show that the shape of the predicted settlement troughs could be explained by the TBM parameters and soil profiles that characterize each group.
机译:本文提出了一种将主成分分析(PCA)与基于自适应神经模糊的推理系统(ANFIS)相结合的方法,以模拟由土压力平衡的TBM引起的地表沉降与运行和地质参数之间的非线性关系。该研究基于在法国图卢兹的地铁B线隧道2号合同的开挖过程中记录的数据。在建模之前,主要成分分析(PCA)和聚集层次聚类(AHC)用于描述TBM参数和地质剖面之间的相互关系。首先,通过绘制不同参考点的沉降剖面图,开发并验证了基于10个所选TBM操作参数(被认为是均匀的地质条件)的ANFIS模型。其次,考虑到地质对定居点的影响,添加了代表土壤类别厚度的5个参数作为输入变量。然后,开发并验证了使用重要主成分作为输入的ANFIS模型。结果表明,尽管分析中使用的数据量很少,但预测和实测沉降之间的相关性很高。此外,该模型能够预测AHC识别出的代表性人群的高斯谷。结果表明,预测沉降槽的形状可以通过表征每个组的TBM参数和土壤剖面来解释。

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