Bayesian model averaging and Bayesian inference-based probabilistic inversion method for arch dam zonal material parameters

Abstract

Inversion analysis based on structural monitoring data is an important part of evaluating the working behaviour of structures. In this paper, a probabilistic inversion method for the elastic modulus of concrete in arch dams based on Bayesian model averaging (BMA) and Bayesian inference is proposed. According to the measured displacement data of an arch dam project, the influence of the separation accuracy of water pressure component, the inversion method and the selection of displacement measuring points on the inversion results of the elastic modulus of the dam body is studied. Example analysis results show that the accuracy of the multi-measurement point displacement and hydraulic pressure component separation model based on BMA-HST (Hydrostatic-seasonal-time) is at least 18.97 % higher than that of the single measurement point, and the relative error of the elastic modulus value of the multi-measurement point zonal inversion based on the Polynomial chaos-Kriging (PCK) proxy model and the Bayesian inference is only 6 % at the maximum, which indicates that the inversion model described in this paper is able to realize the high-precision inversion analysis of the material parameters of the zonal analysis of concrete dams at a relatively low computational cost.