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Bayesian estimation of spatially varying soil parameters with spatiotemporal monitoring data

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Abstract

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.

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(modified from Evans and Lam [17])

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Acknowledgements

The work in this paper was supported by the Natural Science Foundation of China (Project No. 51679135 and No. 51422905) and the Program of Shanghai Academic Research Leader (Project No. 19XD1421900).

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Authors and Affiliations

  1. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, China

    Hao-Qing Yang & Lulu Zhang

  2. Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration (CISSE), Shanghai, China

    Hao-Qing Yang & Lulu Zhang

  3. Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai, China

    Hao-Qing Yang & Lulu Zhang

  4. Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore

    Qiujing Pan, Kok-Kwang Phoon & Zhichao Shen

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  1. Hao-Qing Yang
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  2. Lulu Zhang
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Correspondence to Lulu Zhang.

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Yang, HQ., Zhang, L., Pan, Q. et al. Bayesian estimation of spatially varying soil parameters with spatiotemporal monitoring data. Acta Geotech. 16, 263–278 (2021). https://doi.org/10.1007/s11440-020-00991-z

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