LAUSR

Abstract 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.