MCMC approach for shield tunnel grouting layer estimation using ground penetrating radar

Abstract

Ground penetrating radar (GPR) has been suggested as an effective tool for evaluating the grouting layer behind shield tunnel linings, yet the estimation of the grouting layer thickness is usually difficult. In this paper, we propose a probabilistic inversion method to evaluate the grouting layer using GPR images. This method uses a sliding window along the GPR scan axis combined with a Markov chain Monte Carlo (MCMC) simulation with Bayesian inversion to infer the grouting layer thickness together with the relative permittivity and electric conductivity. We illustrate this approach using a synthetic GPR experiment that simulates grouting layer detection in a shield tunnel along the longitude direction. A sliding window with a width of 0.2 m is used to estimate the model parameters and is moved along the scan axis with a step size of 0.2 m after each inversion. The results demonstrate successful estimation of the grouting layer thickness and its relative permittivity and electric conductivity by the proposed method. Moreover, this method is capable of quantifying uncertainties in the inversion results.