TY - JOUR

T1 - Bayesian updating of soil-water character curve parameters based on the monitor data of a large-scale landslide model experiment

AU - Feng, Chengxin

AU - Tian, Bin

AU - Lu, Xiaochun

AU - Beer, Michael

AU - Broggi, Matteo

AU - Bi, Sifeng

AU - Xiong, Bobo

AU - He, Teng

N1 - Funding Information: This work is supported by the National Key Research and Development Program of China (2017YFC1501100), and the Research Fund for Excellent Dissertation of China Three Gorges University (2019SSPY005). Thanks to the National Research Council and Three Gorges University for their support.

PY - 2020/8/10

Y1 - 2020/8/10

N2 - It is important to determine the soil-water characteristic curve (SWCC) for analyzing landslide seepage under varying hydrodynamic conditions. However, the SWCC exhibits high uncertainty due to the variability inherent in soil. To this end, a Bayesian updating framework based on the experimental data was developed to investigate the uncertainty of the SWCC parameters in this study. The objectives of this research were to quantify the uncertainty embedded within the SWCC and determine the critical factors affecting an unsaturated soil landslide under hydrodynamic conditions. For this purpose, a large-scale landslide experiment was conducted, and the monitored water content data were collected. Steady-state seepage analysis was carried out using the finite element method (FEM) to simulate the slope behavior during water level change. In the proposed framework, the parameters of the SWCC model were treated as random variables and parameter uncertainties were evaluated using the Bayesian approach based on the Markov chain Monte Carlo (MCMC) method. Observed data from large-scale landslide experiments were used to calculate the posterior information of SWCC parameters. Then, 95% confidence intervals for the model parameters of the SWCC were derived. The results show that the Bayesian updating method is feasible for the monitoring of data of large-scale landslide model experiments. The establishment of an artificial neural network (ANN) surrogate model in the Bayesian updating process can greatly improve the efficiency of Bayesian model updating.

AB - It is important to determine the soil-water characteristic curve (SWCC) for analyzing landslide seepage under varying hydrodynamic conditions. However, the SWCC exhibits high uncertainty due to the variability inherent in soil. To this end, a Bayesian updating framework based on the experimental data was developed to investigate the uncertainty of the SWCC parameters in this study. The objectives of this research were to quantify the uncertainty embedded within the SWCC and determine the critical factors affecting an unsaturated soil landslide under hydrodynamic conditions. For this purpose, a large-scale landslide experiment was conducted, and the monitored water content data were collected. Steady-state seepage analysis was carried out using the finite element method (FEM) to simulate the slope behavior during water level change. In the proposed framework, the parameters of the SWCC model were treated as random variables and parameter uncertainties were evaluated using the Bayesian approach based on the Markov chain Monte Carlo (MCMC) method. Observed data from large-scale landslide experiments were used to calculate the posterior information of SWCC parameters. Then, 95% confidence intervals for the model parameters of the SWCC were derived. The results show that the Bayesian updating method is feasible for the monitoring of data of large-scale landslide model experiments. The establishment of an artificial neural network (ANN) surrogate model in the Bayesian updating process can greatly improve the efficiency of Bayesian model updating.

KW - Artificial neural networks

KW - Bayesian updating

KW - Large-scale landslide model experiment

KW - Markov chain Monte Carlo

KW - Soil-water characteristic curve

UR - http://www.scopus.com/inward/record.url?scp=85089815440&partnerID=8YFLogxK

U2 - 10.3390/app10165526

DO - 10.3390/app10165526

M3 - Article

AN - SCOPUS:85089815440

VL - 10

JO - Applied Sciences (Switzerland)

JF - Applied Sciences (Switzerland)

SN - 2076-3417

IS - 16

M1 - 5526

ER -