scholarly journals Bayesian Updating of Soil–Water Character Curve Parameters Based on the Monitor Data of a Large-Scale Landslide Model Experiment

2020 ◽  
Vol 10 (16) ◽  
pp. 5526
Author(s):  
Chengxin Feng ◽  
Bin Tian ◽  
Xiaochun Lu ◽  
Michael Beer ◽  
Matteo Broggi ◽  
...  
Keyword(s):  
Soil Water ◽  
Large Scale ◽  
Model Updating ◽  
Characteristic Curve ◽  
Bayesian Updating ◽  
Model Parameters ◽  
Hydrodynamic Conditions ◽  
Parameter Uncertainties ◽  
Seepage Analysis ◽  
Landslide Model

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.

Soil Water Characteristic Curve for a Granite Residual Soil: Experimental and Numerical Results

2011 ◽  
Vol 312-315 ◽  
pp. 1172-1177 ◽  
Author(s):  
A. Topa Gomes ◽  
A. Viana Da Fonseca ◽  
A. Silva Cardoso
Keyword(s):  
Experimental Data ◽  
Soil Water ◽  
Characteristic Curve ◽  
Residual Soil ◽  
Seepage Analysis ◽  
Soil Water Characteristic Curve ◽  
Granite Residual Soil ◽  
Definition Of ◽  
Paper Method ◽  
Geotechnical Problems

The seepage analysis in geotechnical problems, namely in excavations, was typically performed assuming saturated conditions in the ground. It is now know that the flow in the non saturated part of the ground assumes also relevant importance and hence it is vital to characterize its behaviour. The Soil Water Characteristic Curve (SWCC) of the soil is probably the most important parameter in defining this behaviour and particularly for estimating the permeability of the soil. This paper presents the definition of the SWCC for a granite residual soil using pressure plates and the filter paper method. Based on experimental data some equations are adjusted and the results obtained are discussed. At the end of the paper some predictions of the non saturated permeability of the ground are also performed.

Model for predicting resilient modulus of unsaturated subgrade soil using soil-water characteristic curve

2015 ◽  
Vol 52 (10) ◽  
pp. 1605-1619 ◽  
Author(s):  
Zhong Han ◽  
Sai K. Vanapalli
Keyword(s):  
Moisture Content ◽  
Soil Water ◽  
Resilient Modulus ◽  
Characteristic Curve ◽  
Optimum Moisture Content ◽  
Model Parameters ◽  
Soil Water Characteristic Curve ◽  
Subgrade Soils ◽  
Fine Grained ◽  
Proposed Model

Soil suction (ψ) is one of the key factors that influence the resilient modulus (MR) of pavement subgrade soils. There are several models available in the literature for predicting the MR–ψ correlations. However, the various model parameters required in the existing models are generally determined by performing regression analysis on extensive experimental data of the MR–ψ relationships, which are cumbersome, expensive, and time-consuming to obtain. In this paper, a model is proposed to predict the variation of the MR with respect to the ψ for compacted fine-grained subgrade soils. The information of (i) the MR values at optimum moisture content condition (MROPT) and saturation condition (MRSAT), which are typically determined for use in pavement design practice; (ii) the ψ values at optimum moisture content condition (ψOPT); and (iii) the soil-water characteristic curve (SWCC) is required for using this model. The proposed model is validated by providing comparisons between the measured and predicted MR–ψ relationships for 11 different compacted fine-grained subgrade soils that were tested following various protocols (a total of 16 sets of data, including 210 testing results). The proposed model was found to be suitable for predicting the variation of the MR with respect to the ψ for all the subgrade soils using a single-valued model parameter ξ, which was found to be equal to 2.0. The proposed model is promising for use in practice, as it only requires conventional soil properties and alleviates the need for experimental determination of the MR–ψ relationships.

scholarly journals Bayesian Probabilistic Framework for Damage Identification of Steel Truss Bridges under Joint Uncertainties

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Zheng ◽  
Yi Yu
Keyword(s):  
Damage Identification ◽  
Model Updating ◽  
Element Model ◽  
Truss Structures ◽  
Future Research ◽  
Model Parameters ◽  
Probabilistic Framework ◽  
Parameter Uncertainties ◽  
Truss Bridges ◽  
Uncertain Model

The vibration-based structural health monitoring has been traditionally implemented through the deterministic approach that relies on a single model to identify model parameters that represent damages. When such approach is applied for truss bridges, truss joints are usually modeled as either simple hinges or rigid connections. The former could lead to model uncertainties due to the discrepancy between physical configurations and their mathematical models, while the latter could induce model parameter uncertainties due to difficulty in obtaining accurate model parameters of complex joint details. This paper is to present a new perspective for addressing uncertainties associated with truss joint configurations in damage identification based on Bayesian probabilistic model updating and model class selection. A new sampling method of the transitional Markov chain Monte Carlo is incorporated with the structure’s finite element model for implementing the approach to damage identification of truss structures. This method can not only draw samples which approximate the updated probability distributions of uncertain model parameters but also provide model evidence that quantify probabilities of uncertain model classes. The proposed probabilistic framework and its applicability for addressing joint uncertainties are illustrated and examined with an application example. Future research directions in this field are discussed.

scholarly journals A New Soil-Water Characteristic Curve Model for Unsaturated Loess Based on Wetting-Induced Pore Deformation

Geofluids ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yuwei Zhang ◽  
Zhanping Song ◽  
Xiaolin Weng ◽  
Yongli Xie
Keyword(s):  
Soil Water ◽  
Characteristic Curve ◽  
Shift Factor ◽  
Model Parameters ◽  
Average Pore ◽  
Average Pore Radius ◽  
New Model ◽  
Soil Water Characteristic Curve ◽  
Unsaturated Loess ◽  
The Relationship

The soil-water characteristic curve (SWCC) is the basis for describing seepage, strength, and constitutive model of unsaturated soil. The existing SWCC models do not work accurately for evaluating loess, because they do not consider the pore deformation that is induced by wetting. The present study develops a new SWCC model for unsaturated loess. The model considers the effect of wetting-induced pore deformation (WIPD) on the SWCC. The new model includes 6 parameters, which could be confirmed by laboratory tests. The pore volume function (PVF) was described by the WIPD. The shift factor ξ1i and the compression factor ξ2i were introduced into the model. The relationship between the void ratio e and ξ1i and ξ2i was established using the average pore radius. The new SWCC model for saturated loess was improved based on the classical van Genuchten (V-G) model. If the WIPD had not been considered, the new model would regress into the classical V-G model. SWCC tests of unsaturated loess with different void ratios were carried out to verify the new model. The model parameters were calibrated in the original state, and the SWCCs of different void ratios were predicted by the new model and found to be in good agreement with the test results.

scholarly journals Normalization characteristics of unsaturated undisturbed Ili loess with high level of soluble salt contents

2020 ◽  
Author(s):  
Lisi Niu ◽  
Wenyuan Ren ◽  
Aijun Zhang ◽  
Yuguo Wang ◽  
Jiamin Zhao ◽  
...  
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Critical State ◽  
Large Scale ◽  
Void Ratio ◽  
Characteristic Curve ◽  
Soluble Salt ◽  
Mean Stress ◽  
Compression Tests ◽  
Isotropic Compression

In order to reveal the mechanical characteristics of the unsaturated undisturbed Ili loess in westerly region, the isotropic compression tests controlling suction, the triaxial shrinkage tests controlling net mean stress and consolidation shear tests controlling net confining pressure and suction were carried out under different soluble salt contents. The objective of investigation is to explore the normalized characteristics of compression curve, soil water characteristic curve and critical state line. The results show that the ratio of void ratio to initial void ratio and the ratio of net mean stress to yield net mean stress are suitable to normalize the compression curves under different suctions in the isotropic compression test. The soil water characteristic curves under different net mean stresses in the triaxial shrinkage test can be normalized by the ratio of water content to saturated water content and the ratio of suction to air entry value. In the consolidation shear test controlling constant suction, the unsaturated critical state lines under different suctions can be normalized by the corresponding saturated critical state line in the plane of effective net mean stress and deviator stress. The unsaturated critical state lines under different suctions in the plane of void ratio and net mean stress can be normalized by means of degree of gas saturation and the ratio of unsaturated void ratio to saturated void ratio under the same effective net mean stress. The results provide potential benefits for the constructions of large-scale water conservancy projects in the special area of Central Asia.

SOIL WATER CHARCTERISTIC CURVES OF BENTONITES IN ISOCHORIC CONDITIONS DURING WETTING: MEASUREMENT AND PREDICTION

2020 ◽  
Author(s):  
Tadikonda Venkata Bharat ◽  
Yagom Gapak
Keyword(s):  
Soil Water ◽  
Unsaturated Flow ◽  
Characteristic Curve ◽  
Dry Density ◽  
Model Parameters ◽  
Empirical Relationships ◽  
Flow Simulations ◽  
Proposed Model ◽  
Theoretical Procedure

Determination of soil water characteristic curve (SWCC) of compacted bentonites in the isochoric condition is a prerequisite for unsaturated flow simulations in several geoenvironmental applications. The SWCC data are, however, not readily available for many compacted bentonites over a wide suction range due to difficulties associated with the testing. In this work, wetting SWCCs of four Indian bentonites of different plasticity were established experimentally at compaction dry densities of 1.4, 1.6, and 1.8 Mg/m3 in isochoric conditions using two independent laboratory techniques in different suction ranges. The modified Kovács (MK) model was extended to simulate the SWCC data of the Indian bentonites. Empirical relationships between MK model parameters and compaction dry density for these bentonites were obtained. A theoretical procedure for the estimation of SWCCs of the compacted bentonites based on the basic bentonite properties and proposed correlations was validated on the available wetting SWCC data of compacted GMZ, MX80, and FEBEX bentonites from the literature studies. The proposed model is useful for predicting the the SWCCs of bentonites from basic clay properties in the absence of measured data and in understanding the influence of dry density and plasticity on SWCC.

scholarly journals Spatial variability of the parameters of soil-water characteristic curves in gravel-mulched fields

2019 ◽  
Vol 20 (1) ◽  
pp. 231-239
Author(s):  
Wenju Zhao ◽  
Taohong Cao ◽  
Zongli Li ◽  
Yu Su ◽  
Zhiwei Bao
Keyword(s):  
Spatial Variability ◽  
Soil Water ◽  
Water Retention ◽  
Characteristic Curve ◽  
Model Parameters ◽  
Soil Hydraulic Properties ◽  
Geostatistical Methods ◽  
Classical Statistics ◽  
Van Genuchten Model ◽  
Soil Water Characteristic Curves

Abstract Knowledge of the soil-water characteristic curve (SWCC) and its spatial variability is essential for many agricultural, environmental, and engineering applications. We analyzed the spatial variability of the parameters of SWCC in gravel-mulched fields using classical statistics and geostatistical methods. Soil samples were collected from the layer in 64 evenly distributed 1 × 1 m quadrats 4 m apart, center to center. SWCC in the gravel-mulched fields could be fitted well by both the van Genuchten and Brooks–Corey models, but the fit was better with the van Genuchten model. The type of fitting three parameters was tested. The model parameters θs and n of each type of soil were weakly variable, and α was moderately variable. The results indicate that the gravel-mulched field has better water retention, and the water retention effect of the new gravel-mulched fields is most obvious. The spatial variation of the parameters in SWCC can therefore be used to infer soil hydraulic properties, which is important for simplifying the calculation of SWCC and quantitatively determining the retention of soil water and for managing the capacity of soil to retain water in gravel-mulched fields in arid regions.

scholarly journals Towards Uncertainty Quantification of LES and URANS for the Buoyancy-Driven Mixing Process between Two Miscible Fluids—Differentially Heated Cavity of Aspect Ratio 4

Fluids ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 161
Author(s):  
Philipp J. Wenig ◽  
Ruiyun Ji ◽  
Stephan Kelm ◽  
Markus Klein
Keyword(s):  
Aspect Ratio ◽  
Boundary Conditions ◽  
Uncertainty Quantification ◽  
Large Scale ◽  
Method Development ◽  
Model Parameters ◽  
Mixing Process ◽  
Reactor Accident ◽  
Hydrogen Distribution ◽  
Parameter Uncertainties

Numerical simulations are subject to uncertainties due to the imprecise knowledge of physical properties, model parameters, as well as initial and boundary conditions. The assessment of these uncertainties is required for some applications. In the field of Computational Fluid Dynamics (CFD), the reliable prediction of hydrogen distribution and pressure build-up in nuclear reactor containment after a severe reactor accident is a representative application where the assessment of these uncertainties is of essential importance. The inital and boundary conditions that significantly influence the present buoyancy-driven flow are subject to uncertainties. Therefore, the aim is to investigate the propagation of uncertainties in input parameters to the results variables. As a basis for the examination of a representative reactor test containment, the investigations are initially carried out using the Differentially Heated Cavity (DHC) of aspect ratio 4 with Ra=2×109 as a test case from the literature. This allows for gradual method development for guidelines to quantify the uncertainty of natural convection flows in large-scale industrial applications. A dual approach is applied, in which Large Eddy Simulation (LES) is used as reference for the Unsteady Reynolds-Averaged Navier–Stokes (URANS) computations. A methodology for the uncertainty quantification in engineering applications with a preceding mesh convergence study and sensitivity analysis is presented. By taking the LES as a reference, the results indicate that URANS is able to predict the underlying mixing process at Ra=2×109 and the variability of the result variables due to parameter uncertainties.

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