Based on the solid-fluid coupling theory and Bayesian theory

a coupled probabilistic back analysis model is developed for an unsaturated soil slope. A method of multi-objective probabilistic inverse analysis using time-varied data of displacement and pore water pressure is proposed based on Markov chain theory. The results of the multi-objective inverse analysis and single-objective inverse analysis found that the posterior standard deviations of the input parameters obtained by multi-objective inverse analysis are smaller than the single-objective cases. It is also found that the results obtained by single-objective agree with the measurement well but have an unsatisfied prediction on other objectives. Multi-objective inverse analysis could optimize all the objectives simultaneously and the results of inverse analysis can meet well with all the objectives. For the multi-objectives case

95% uncertainty bounds are narrower than the single-objective cases

and the soil parameters obtained by the multi-objective probabilistic inverse analysis using different types of data are more reasonable

and the prediction using the results of the multi-objective probabilistic inverse analysis is more correct than the single-objective probabilistic inverse analysis results.