Abstract
Considering the deformation of discrete monitoring points within the same deformable body usually have similar physical properties and tend to undergoing identical dynamic process, joint modelling of the deformation processes of these points in time domain are expected to generate better results. Yin et al. (1997) first extended the multi-variable grey model-system cloud grey model SCGM(1,m), with obviously superior modelling mechanism than single-variable grey model, to multi-point deformation modelling. However, this model is still not widely recognized and its applications remain very limited in the field of deformation analysis. The objective of this study is to demonstrate the capability of the SCGM(1,m) model, to present two revisions to further improve the performance of the model and to draw more attention to the community of deformation analysis. We first introduce the principles of the SCGM(1,m) model in the analysis and prediction of deformation surveys. Two practical techniques, namely residuals re-modelling and linear regression adjustment, are then presented to improve the SCGM(1,m) model. Combined with slope monitoring data, the modelling with the original and the improved SCGM(1,m) models by residuals re-modelling and linear regression adjustment are illustrated. The mean relative prediction errors decrease from 5.89% to 3.54% and 2.69%, when the two refining techniques are applied, respectively, indicating relative improvements of 39.9% and 54.3%.
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Wang, Qj., Wang, Cc., Xie, Ra. et al. An improved SCGM(1,m) model for multi-point deformation analysis. Geosci J 18, 477–484 (2014). https://doi.org/10.1007/s12303-014-0012-z
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DOI: https://doi.org/10.1007/s12303-014-0012-z