Stochastic Inversion Method for In-service Underground Structure Load Based on Full Bayesian Inference

Abstract:

A stochastic inversion method for in-service underground structure load was proposed. Firstly， based on spline function， the disorderly distributed load was parameterized into a set of interpolation unknowns. Secondly， on Bayesian framework， the posterior probability density function （PDF） of the unknowns was built by incorporating the measured deformation data. Lastly， the full Bayesian inference of the corresponding unknown load was carried out based on an efficient sampling method of DREAM（differential evolution adaptive metropolis）. Testing results from a field case indicated that the expectation load obtained from the proposed method fits well with the actual recorded pressures while the ill-conditioning is encountered by traditional deterministic inversion method. Apart from the expectation load， complete PDFs of the inversion load are obtained， which presents the natural advantage of the proposed method to deal with non-uniqueness.