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The method of pseudo-static analysis has been widely used to perform seismic slope stability, in which a seismic coefficient is used to represent the earthquake shaking effect. However, it is important but difficult to select the magnitude of seismic coefficients, which are inevitably subjected to different levels of uncertainties. This paper aimed to study the influences of seismic coefficient uncertainties on pseudo-static slope stability from the perspective of probabilistic sensitivity analysis. The deterministic critical slope height was estimated by the method of upper-bound limit analysis with the method of pseudo-static analysis. The soil shear strength parameters, the slope geometrical parameters (including slope inclinations, slope heights, and the slope widths), the horizontal seismic acceleration coefficient, and the unit weight of soil masses were considered as random variables. The influences of their uncertainty degrees, the correlation relations, and the distribution types of random variables on probabilistic density functions, failure probabilities, and sensitivity analysis were discussed. It was shown that the uncertainty degrees greatly impact the probability density distributions of critical slope heights, the computed failure probabilities, and Sobol’ index, and the horizontal seismic coefficient was the second most important variable compared to the soil shear strength parameters.
Dongli Li; Miaojun Sun; Echuan Yan; Tao Yang. The Effects of Seismic Coefficient Uncertainty on Pseudo-Static Slope Stability: A Probabilistic Sensitivity Analysis. Sustainability 2021, 13, 8647 .
AMA StyleDongli Li, Miaojun Sun, Echuan Yan, Tao Yang. The Effects of Seismic Coefficient Uncertainty on Pseudo-Static Slope Stability: A Probabilistic Sensitivity Analysis. Sustainability. 2021; 13 (15):8647.
Chicago/Turabian StyleDongli Li; Miaojun Sun; Echuan Yan; Tao Yang. 2021. "The Effects of Seismic Coefficient Uncertainty on Pseudo-Static Slope Stability: A Probabilistic Sensitivity Analysis." Sustainability 13, no. 15: 8647.
The creep properties of slip zone soil are critical to deformation prediction and slope stability analysis. A series of triaxial drained creep tests were conducted on the slip band soil from a creeping landslide. The results indicate that soil creep occurs in two stages, with confining and deviatoric stresses being critical factors. The long-term strength of the soil was estimated to be 60–75 % of the conventional strength according to the isochronous curves. The soil creep characteristics were used to predict the creep strain using both the Burgers and the Singh–Mitchell model, and large discrepancies were found between the predicted strain and test results. Accordingly, a new empirical model based on the Morgan Mercer Flodin growth model has been developed to describe the creep behavior of gravely clay in the slip zone. Four parameters of this model are estimated by nonlinear regression. The deformations predicted by this model are in reasonable agreement with experimental data.
Miaojun Sun; Huiming Tang; Mingyuan Wang; Zhigang Shan; Xinli Hu. Creep behavior of slip zone soil of the Majiagou landslide in the Three Gorges area. Environmental Earth Sciences 2016, 75, 1199 .
AMA StyleMiaojun Sun, Huiming Tang, Mingyuan Wang, Zhigang Shan, Xinli Hu. Creep behavior of slip zone soil of the Majiagou landslide in the Three Gorges area. Environmental Earth Sciences. 2016; 75 (16):1199.
Chicago/Turabian StyleMiaojun Sun; Huiming Tang; Mingyuan Wang; Zhigang Shan; Xinli Hu. 2016. "Creep behavior of slip zone soil of the Majiagou landslide in the Three Gorges area." Environmental Earth Sciences 75, no. 16: 1199.
This paper presents an integrated approach that predicts the microparameters of the particle flow code in three dimensions (PFC3D) model in triaxial compression simulations. The new approach combines a full factorial design (FFD) with an artificial neural network (ANN). The ANN model maps the input factors (triaxial compressive strength, Poisson’s ratio, and Young’s modulus) onto output variables, which are microparameters that affect the macroscopic responses in a PFC3D model. Emphasis is placed on data collection and optimization of the ANN model using FFD. The data for training and testing the ANN model were obtained from laboratory experiments and numerical simulations of a PFC3D model according to the principles of FFD. Using a backpropagation artificial neural network (BPNN) optimized with FFD principles, the object of the current study (to reliably predict the microparameters for a PFC3D model) has been achieved because the predicting data obtained by the BPNN model were in excellent agreement with the testing data.
Miao-Jun Sun; Hui-Ming Tang; Xin-Li Hu; Yun-Feng Ge; Sha Lu. Microparameter Prediction for a Triaxial Compression PFC3D Model of Rock Using Full Factorial Designs and Artificial Neural Networks. Geotechnical and Geological Engineering 2013, 31, 1249 -1259.
AMA StyleMiao-Jun Sun, Hui-Ming Tang, Xin-Li Hu, Yun-Feng Ge, Sha Lu. Microparameter Prediction for a Triaxial Compression PFC3D Model of Rock Using Full Factorial Designs and Artificial Neural Networks. Geotechnical and Geological Engineering. 2013; 31 (4):1249-1259.
Chicago/Turabian StyleMiao-Jun Sun; Hui-Ming Tang; Xin-Li Hu; Yun-Feng Ge; Sha Lu. 2013. "Microparameter Prediction for a Triaxial Compression PFC3D Model of Rock Using Full Factorial Designs and Artificial Neural Networks." Geotechnical and Geological Engineering 31, no. 4: 1249-1259.