Improved deformation modelling of structures by least-squares variance component estimation based on multi-sensor data integration

Abstract

In this contribution, to improve the deformation modelling based on data integration, the LS-VCE algorithm is proposed by obtaining a stochastic model of input multi-sensor data. So, one can achieve the accurate variance-covariance matrix of multi-sensor observations to participate in iterative least-squares. A practical application was made for the settlement observations from geotechnical settlement-meters and geodetic levelling (respectively known as internal and external sensors) to model the surface settlement variation of the Karkhe earth-dam. The determined variance component shows less contribution of the geotechnical settlements in the deformation modelling. An achievement of this paper is that the LS-VCE method improves the integration of the geotechnical with geodetic data by estimating an optimal stochastic model resulting in deformation model optimization. Validation results of estimated surface settlements on the check-points show an RMSE of about 3 cm and a relative-error of about 14%, which indicates the success of the modelling.