Bayesian void detection of the CA mortar layer in the slab track considering temperature effect

This paper proposes a void detection method for the cement-emulsified asphalt (CA) mortar layer of the slab track structure utilizing Markov chain Monte Carlo-based Bayesian model updating technique considering the temperature effect. The mechanical properties of CA mortar are susceptible to the temperature variations, which is one of the main challenges for the CA void detection based on Bayesian model updating. Nearly a half year temperature monitoring on the CA mortar layer was conducted and comprehensive numerical and experimental case studies were carried out to demonstrate the feasibility and applicability of the proposed Bayesian CA void detection method considering the temperature effect. The uncertain model parameters can be evaluated from the Monte Carlo discrete samples by using kernel density estimation, and the associated uncertainties of the uncertain model parameters can be quantitatively described by calculating the posterior probability density functions of the model parameters of the CA mortar layer. Positive investigations can be obtained from both the numerical and experimental cases, which indicates that the proposed method is robust to detect the CA void.