An improved SCGM(1,m) model for multi-point deformation analysis

Considering the deformation of discrete monitoringpoints within the same deformable body usually have similar physicalproperties and tend to undergoing identical dynamic process, jointmodelling of the deformation processes of these points in time domainare expected to generate better results. Yin et al. (1997) first extendedthe multi-variable grey model-system cloud grey model SCGM(1,m),with obviously superior modelling mechanism than single-variablegrey model, to multi-point deformation modelling. However, thismodel is still not widely recognized and its applications remain verylimited in the field of deformation analysis. The objective of this studyis to demonstrate the capability of the SCGM(1,m) model, to presenttwo revisions to further improve the performance of the model andto draw more attention to the community of deformation analysis. We first introduce the principles of the SCGM(1,m) model in theanalysis and prediction of deformation surveys. Two practicaltechniques, namely residuals re-modelling and linear regressionadjustment, are then presented to improve the SCGM(1,m) model. Combined with slope monitoring data, the modelling with the originaland the improved SCGM(1,m) models by residuals re-modellingand linear regression adjustment are illustrated. The mean relativeprediction errors decrease from 5.89% to 3.54% and 2.69%, whenthe two refining techniques are applied, respectively, indicating relativeimprovements of 39.9% and 54.3%.