Bayesian estimation of spatially varying soil parameters with spatiotemporal monitoring data

The characterization of in situ ground conditions is essential for geotechnical practice. The probabilistic estimation of soil parameters can be achieved via updating with monitoring data within the Bayesian framework. The estimation of spatially varying soil parameters is seldom undertaken with time-variant monitoring data. In this study, an efficient Bayesian method is presented for the estimation of spatially varied saturated hydraulic conductivity k(s) of unsaturated soil slope with spatiotemporal monitoring data. The computationally cheap surrogate model of the adaptive sparse polynomial chaos expansion method is adopted to approximate the transient numerical model. Markov chain Monte Carlo method is used for the probabilistic estimation of basic random variables. Based on the hypothetical cases, the effects of monitoring frequency and stage are studied. The errors and the uncertainties of the estimated k(s) fields are increased with the decreasing monitoring frequency. Bayesian estimation of spatial variability is more accurate when using the later stage of monitoring data. The estimated method is further verified with a real case study by the comparison of borehole data, dynamic probe test (DPT) data, and field monitoring data. The distribution of the soil types acquired from boreholes is reflected in the estimated k(s). The estimated field of k(s) has a certain agreement with the borehole log and DPT measurements and can reproduce the spatial variability of the site to an acceptable degree.

Automated summary

This study presents an efficient Bayesian method for estimating spatially varied saturated hydraulic conductivity k(s) of unsaturated soil slopes using spatiotemporal monitoring data. The accuracy of Bayesian estimation of spatial variability is improved by using later-stage monitoring data. Decreasing monitoring frequency increases errors and uncertainties in estimated k(s) fields. The estimated method is further validated with a real case study, showing agreement with borehole data, dynamic probe test results, and field monitoring data.