A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
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 ks 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 ks 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 ks. The estimated field of ks has a certain agreement with the borehole log and DPT measurements and can reproduce the spatial variability of the site to an acceptable degree.
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 ks 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 ks 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 ks. The estimated field of ks has a certain agreement with the borehole log and DPT measurements and can reproduce the spatial variability of the site to an acceptable degree.
Bayesian estimation of spatially varying soil parameters with spatiotemporal monitoring data
Acta Geotech.
Yang, Hao-Qing (author) / Zhang, Lulu (author) / Pan, Qiujing (author) / Phoon, Kok-Kwang (author) / Shen, Zhichao (author)
Acta Geotechnica ; 16 ; 263-278
2021-01-01
16 pages
Article (Journal)
Electronic Resource
English
Characterization , Monitoring data , Polynomial chaos , Rainfall-induced landslide , Spatial variability , Uncertainty , Unsaturated soil Engineering , Geoengineering, Foundations, Hydraulics , Solid Mechanics , Geotechnical Engineering & Applied Earth Sciences , Soil Science & Conservation , Soft and Granular Matter, Complex Fluids and Microfluidics
Bayesian estimation of spatially varying soil parameters with spatiotemporal monitoring data
| Springer Verlag | 2021
Calibration of Soil Constitutive Models with Spatially Varying Parameters
| Online Contents | 2007
Calibration of Soil Constitutive Models with Spatially Varying Parameters
| British Library Online Contents | 2007
| Taylor & Francis Verlag | 2022
Bayesian Estimation of Soil Parameters from Radar Backscatter Data
| Online Contents | 1996