Bayesian inference of spatially varying parameters in soil constitutive models by using deformation observation data

Abstract Parameters of soil constitutive models are usually identified through laboratory tests. The spatial variability of these parameters is generally not considered due to the limitation of the test scale. This study proposes a data‐driven approach to infer the spatially varying parameter of the modified Cam‐clay model from limited field observations and subsequently improves soil settlement predictions. The observation data and numerical results of random finite element method are assimilated in an inverse modeling process based on the iterative Ensemble Kalman filtering (iEnKF). Different unknown variables and number of observations are used to study their effects on parameter estimations and settlement predictions. The effectiveness of the proposed approach is illustrated through a synthetic partial‐loading test. The results show that the site‐specific spatial variability can be estimated reasonably, and predictions of settlement can be improved by using the inferred parameter field. When the variables to be inferred change from all 60 variables to the selected 17 important variables, the average error of the estimated fields increases, but the variance decreases. A reduction in the observation spacing and an increase in the number of observations lead to a slightly smaller error of the mean and considerably reduced uncertainties of soil parameters. Although the inferred results of parameter field show different accuracies, the corresponding calculated settlements are generally similar and satisfactory.