Deformations in tunnels and galleries due to confinement pressures can occur continuously but also at discrete time intervals. We propose a methodology to detect discrete significant deformations in tunnels based on a probabilistic model-based curve clustering. An EM (expectation-maximization) algorithm is used to obtain the parameters of the component density functions that maximize the log-likelihood function. The estimation of the number of clusters was performed by means of the Bayesian Information Criterion (BIC).
The proposed methodology was applied to the analysis of the deformations in a tunnel that has been used in the past to transport coal in an underground mine. A set of 40 profiles measured over a period of 20 months were compared. The results obtained show that deformations are not continuous but significantly high deformation episodes occur.