Uncertainty Of Soil Parameters Research Articles

Mean Parametric Monte Carlo Study of Fill Slopes

AbstractSlope stability analysis has traditionally been performed using a deterministic approach. However, it has strongly been debated that the use of only the factor of safety in slope stability analysis does not explicitly account for all the uncertainties in soil parameters. Therefore, to investigate the effect of uncertainties in the stability of a fill slope, this paper uses the finite-element limit analysis methods to conduct a simple probabilistic-based analysis of two-layered undrained clay slopes. The results obtained show that although the recently developed stability charts for two-layered undrained clay may be convenient tools, the factors of safety calculated from the charts may not be sufficient. Particularly, it is shown that the probability of failure of this type of slope can be highly influenced by uncertainties in the soil properties. Various probabilistic charts based on different coefficients of variation in soil properties have been proposed in this paper. Based on this study, the r...

Reliability-Based Design Optimization Strategy for Soil Tillage Equipment Considering Soil Parameter Uncertainty

The objective of this work is to provide an efficient tool of Reliability-Based Design Optimization (RBDO) for soil tillage machine design process in order to achieve designs with a required reliability (safety) level. An efficient methodology that controls the reliability levels for different statistical distribution cases of random soil properties is developed. This developed strategy is based on design sensitivity concepts in order to determine the influence of each random parameter. The application of this method consists in taking into account the uncertainties on the soil tillage forces. The tillage forces are calculated in accordance with analytical model of McKyes and Ali with some modifications to include the effect of both soil-metal adhesion and tool speed. The different results show the importance of the developed strategy to improve the performance of the soil tillage equipments considering both random geometry and loading parameters.

Quantitative liquefaction-induced lateral spread hazard mapping

To quantitatively map regional lateral spread hazard considering a complete portfolio of possible earthquakes is fundamental for a seismic risk assessment over large spatial coverage. This paper presents a probabilistic model of liquefaction-induced lateral spread considering seismic randomness and uncertainty of soil properties. This model enables quantitative maps of lateral spread displacement at any exceedance probability in a specified time period, or exceedance probability of any pre-defined displacement threshold. Two mapping procedures are illustrated through a case study by generating hazard maps of lateral spread based on available cone penetration test data. These maps highlight the hotspots susceptible to serious lateral spread displacements, providing useful information on need of project-specific in-depth geotechnical investigation. The mapping procedure is based on either geostatistical interpolation or stochastic simulation. The geostatistical interpolation method has the merit to be less computationally expensive, while the stochastic simulation method seems more rational as it explicitly model the uncertainty of soil parameters and their spatial structure.

Accounting for soil parameter uncertainty in a physically based and distributed approach for rainfall-triggered landslides

In this study we propose a probabilistic approach for coupled distributed hydrological-hillslope stability models that accounts for soil parameters uncertainty at basin scale. The geotechnical and soil retention curve parameters are treated as random variables across the basin and theoretical probability distributions of the Factor of Safety (FS) are estimated. The derived distributions are used to obtain the spatio-temporal dynamics of probability of failure, in terms of parameters uncertainty, conditioned to soil moisture dynamics. The framework has been implemented in the tRIBS-VEGGIE (Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator-VEGetation Generator for Interactive Evolution)-Landslide model and applied to a basin in the Luquillo Experimental Forest (Puerto Rico) where shallow landslides are common. In particular, the methodology was used to evaluate how the spatial and temporal patterns of precipitation, whose variability is significant over the basin, affect the distribution of probability of failure, through event scale analyses. Results indicate that hyetographs where heavy precipitation is near the end of the event lead to the most critical conditions in terms of probability of failure. Copyright © 2015 John Wiley & Sons, Ltd.

On the treatment of soft soil parameter uncertainties in planetary rover mobility simulations

Nowadays soft soil–wheel contact models are widely used for predicting the mobility of rovers in off-road applications. However, most of the contact models used in computer simulations are based on semi-empirical laws for which soil parameters can be assessed only with large uncertainty. This lack of knowledge results in significant uncertainty on the rover position predictions. Applied to a planetary rover model, this paper illustrates probabilistic and non-probabilistic techniques for efficient treatment of soil parameter uncertainty for rover position predictions.

System reliability analysis for anchor-stabilised slopes considering stochastic corrosion of anchors

In this paper, a methodology using Monte Carlo simulation method was developed in conjunction with the modified limiting equilibrium method of slices. Additionally, the time-dependent deterioration of bond capacity of corroding soil anchors was developed in this methodology due to the attack of chlorides. Uncertainties of anchor-related variables and soil parameters were considered using pertinent statistical descriptors. Furthermore, the inherent stochastic nature of the corrosion and degradation process for the anchor/slope system was identified and incorporated in the simulation. A PC-based software, C-UASLOPE, has been coded to allow for handling of complex slope geometry, soil profile and groundwater conditions. An illustrative example was presented to clarify the application of the C-UASLOPE software in modelling the deterioration process to aid in determining the system reliability. The importance of using a system reliability-based algorithm was illustrated by showing that the anchor/slope system may fail unpredictably during design life – even though the initial design results have a high degree of safety – if uncertainties of soil parameters, anchor force and the time-dependent stochastic corrosion process are not accounted for systematically.

Probabilistic modeling of rainfall-induced shallow landslide using a point-estimate method

The assessment of rainfall-induced shallow landslides using a physical-based model requires accounting for the uncertainty of soil parameters that cannot be accurately quantified. In this study, the modified Iverson model was integrated with the Rosenblueth point-estimate method to probabilistically model rainfall-induced shallow landslides, while simultaneously considering the uncertainty and correlation of soil parameters. Using various soil parameters, hillslope conditions, and hydrological conditions, the applicability of the Rosenblueth point-estimate method was compared with that of the Monte Carlo simulation method. The simulated results indicated that the Rosenblueth point-estimate method can accurately and efficiently assess the probability of rainfall-induced shallow landslides. A lognormal distribution of the safety factor can be assumed when the soil parameters are lognormally distributed random variables. Accounting for the correlation of soil parameters decreases the standard deviation of the safety factor, but does not affect the mean of the safety factor. Neglecting the correlation of soil parameters could substantially misestimate the probability of rainfall-induced shallow landslides.

Probabilistic model calibration for soil slope under rainfall: effects of measurement duration and frequency in field monitoring

Field measured data reflect real response of soil slopes under rainfall infiltration. Based on field measured pore-water pressure histories, prediction models for slope design can be calibrated. In this study, a probabilistic approach based on the Bayesian theory is adopted to calibrate a slope-stability model using time-varying response data and to justify appropriate monitoring duration and data recording interval for field monitoring programmes. As an illustrative case study, field measured pore-water pressure responses in a natural terrain slope in Hong Kong are used to calibrate a prediction model using the Markov chain Monte Carlo simulation method. It is found that with the increase of measurement duration for model calibration, the uncertainties of soil parameters can be reduced. The duration of the calibrated data should be sufficiently long so as not to underestimate the model error. With regard to the effect of measurement interval, both the uncertainties of input parameters and the model error are increased with the increase of data recording interval. The correlations among the soil parameters are subject to greater uncertainties and the prediction accuracy of the calibrated model is reduced with a larger recording interval.

Reliability‐based design for slopes reinforced with a row of drilled shafts

SUMMARYIn this paper, a reliability‐based computational algorithm was developed and coded into a computer program, P‐UASLOPE, for design of a row of equally spaced drilled shafts to stabilize an unstable slope while achieving the required target reliability index with minimum volume of drilled shafts. The Monte Carlo simulation technique was used in the previously developed deterministic computational program, in which the limiting equilibrium method of slices was modified to incorporate the arching effects of the drilled shafts in a slope. Uncertainties of soil parameters in the slope were considered by statistical descriptors, including mean, coefficient of variance, and distribution function. Model errors of the semi‐empirical predictive equation for the load transfer factor for characterizing the soil arching effects were considered by statistics of bias. A total of 41 cases of 3‐D finite element simulation results were used to determine the statistics of bias. Two design examples, one is a two‐layer slope and the other one is an actual failed slope, were given to demonstrate the use of P‐UASLOPE program for optimized design of drilled shafts reinforced slope system to achieve the most economic combination of design variables (i.e., location, spacing, diameter, and length of drilled shafts) to satisfy the design requirements in terms of target reliability index and the structural performance of the drilled shafts. A single value of factor of safety chosen in the deterministic approach may not yield the desired level of reliability as uncertainties of soil parameters and model errors cannot be accounted for systematically. Copyright © 2013 John Wiley & Sons, Ltd.

Spatial Prediction of N2O Emissions in Pasture: A Bayesian Model Averaging Analysis

Nitrous oxide (N2O) is one of the greenhouse gases that can contribute to global warming. Spatial variability of N2O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N2O – environmental factors relationships. Few researches have examined the impacts of various spatial correlation structures (e.g. independence, distance-based and neighbourhood based) on spatial prediction of N2O emissions. This study aimed to assess the impact of three spatial correlation structures on spatial predictions and calibrate the spatial prediction using Bayesian model averaging (BMA) based on replicated, irregular point-referenced data. The data were measured in 17 chambers randomly placed across a 271 m2 field between October 2007 and September 2008 in the southeast of Australia. We used a Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR) model to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N2O emissions across the study site. We compared these with a Bayesian regression model with independent errors. The three approaches resulted in different derived maps of spatial prediction of N2O emissions. We found that incorporating spatial dependency in the model not only substantially improved predictions of N2O emission from soil, but also better quantified uncertainties of soil parameters in the study. The hybrid model structure obtained by BMA improved the accuracy of spatial prediction of N2O emissions across this study region.

Investigating a method for estimating direct nitrous oxide emissions from grazed pasture soils in New Zealand using NZ-DNDC

In this study, we developed emission factor (EF) look-up tables for calculating the direct nitrous oxide (N2O) emissions from grazed pasture soils in New Zealand. Look-up tables of long-term average direct emission factors (and their associated uncertainties) were generated using multiple simulations of the NZ-DNDC model over a representative range of major soil, climate and management conditions occurring in New Zealand using 20years of climate data. These EFs were then combined with national activity data maps to estimate direct N2O emissions from grazed pasture in New Zealand using 2010 activity data. The total direct N2O emissions using look-up tables were 12.7±12.1Gg N2O–N (equivalent to using a national average EF of 0.70±0.67%). This agreed with the amount calculated using the New Zealand specific EFs (95% confidence interval 7.7–23.1Gg N2O–N), although the relative uncertainty increased. The high uncertainties in the look-up table EFs were primarily due to the high uncertainty of the soil parameters within the selected soil categories. Uncertainty analyses revealed that the uncertainty in soil parameters contributed much more to the uncertainty in N2O emissions than the inter-annual weather variability. The effect of changes to fertiliser applications was also examined and it was found that for fertiliser application rates of 0–50kg N/ha for sheep and beef and 60–240kg N/ha for dairy the modelled EF was within ±10% of the value simulated using annual fertiliser application rates of 15kg N/ha and 140kg N/ha respectively.

Bored pile design in stiff clay I: codes of practice

The assessment of the allowable bearing load of bored piles ‘floating' in stiff clay is a standard engineering task. Although the soil mechanics is universal, engineers designing structures in different parts of the world will need to take into account the pertinent codes of practice. It will be helpful to compare such codes, especially in relation to their treatment of uncertainty in the design of bored piles. This paper presents a series of design calculations for a real set of geotechnical data using four international codes of practice: the Australian, American, European and Russian codes. The National Annexes of Ireland, the Netherlands and the UK are used in conjunction with the European code. This selection of countries covers the three Eurocode 7 design approaches (DA1, DA2 and DA3). A non-codified design method is used to provide a base case for comparative purposes with the six codified calculations. A companion paper investigates the issues of soil mechanics in pile design methods, uncertainty in soil parameters and settlement criteria.

Assessing the Role of Parameter and Input Uncertainty in Ecohydrologic Modeling: Implications for a Semi-arid and Urbanizing Coastal California Catchment

Ecohydrologic models are a key tool in understanding plant–water interactions and their vulnerability to environmental change. Although implications of uncertainty in these models are often assessed within a strictly hydrologic context (for example, runoff modeling), the implications of uncertainty for estimation of vegetation water use are less frequently considered. We assess the influence of commonly used model parameters and inputs on predictions of catchment-scale evapotranspiration (ET) and runoff. By clarifying the implications of uncertainty, we identify strategies for insuring that the quality of data used to drive models is considered in interpretation of model predictions. Our assessment also provides insight into unique features of semi-arid, urbanizing watersheds that shape ET patterns. We consider four sources of uncertainty: soil parameters, irrigation inputs, and spatial extrapolation of both point precipitation and air temperature for an urbanizing, semi-arid coastal catchment in Santa Barbara, CA. Our results highlight a seasonal transition from soil parameters to irrigation inputs as key controls on ET. Both ET and runoff show substantial sensitivity to uncertainty in soil parameters, even after parameters have been calibrated against observed streamflow. Sensitivity to uncertainty in precipitation manifested primarily in winter runoff predictions, whereas sensitivity to irrigation manifested exclusively in modeled summer ET. Neither ET nor runoff was highly sensitive to uncertainty in spatial interpolation of temperature. Results argue that efforts to improve ecohydrologic modeling of vegetation water use and associated water-limited ecological processes in these semi-arid regions should focus on improving estimates of anthropogenic outdoor water use and explicit accounting of soil parameter uncertainty.

Estimating the potential impact of vegetation on the water cycle requires accurate soil water parameter estimation

It is well known that vegetation dynamics at the catchment scale depends on the prevailing weather and soil moisture conditions. Soil moisture, however, is not equally distributed in space due to differences in topography, weather patterns, soil properties and the type and amount of vegetation cover. To elucidate the complex interaction between vegetation and soil moisture, the dynamic vegetation model LPJ-GUESS ( Smith et al., 2001), which provides estimations of vegetation dynamics, but does not consider lateral water fluxes was coupled with the hydrological TOPMODEL (cf. Beven, 2001) in order to be able to evaluate the importance of these lateral fluxes. The new model LG-TM was calibrated and validated in two climatically different mountain catchments. The estimations of runoff were good, when monthly and weekly time scales were considered, although the low flow periods at winter time were somewhat underestimated. The uncertainty in the climate induced change vegetation carbon storage caused by the uncertainty in soil parameters was up to 3–5 kg C m −2 (depending on elevation and catchment), compared to the total change in vegetation carbon storage of 5–9 kg C m −2. Therefore accurate estimates of the parameters influencing the water holding capacity of the soil, for example depth and porosity, are necessary when estimating future changes in vegetation carbon storage. Similarly, changes in plant transpiration due to climatic changes could be almost double as high (88 mm m −2) in the not calibrated model compared to the new model version (ca 50 mm m −2 transpiration change). The uncertainties in these soil properties were found to be more important than the lateral water exchange between grid cells, even in steep topography at least for the temporal and spatial resolution used here.

Impact of uncertainties in meteorological forcing data and land surface parameters on global estimates of terrestrial water balance components

AbstractThe quality of near‐surface meteorology and land surface parameters strongly influences the simulation of terrestrial water balance components by land surface models. In this article, the sensitivity of global estimates of terrestrial water balance components to uncertainties in meteorological forcing data and land surface parameters is investigated using the SWAP land surface model and different global datasets. The latter were prepared within the framework of the International Satellite Land‐Surface Climatology Project (ISLSCP) Initiative II and the Second Global Soil Wetness Project (GSWP‐2). Sixteen variants of estimations of mean annual water balance components on the global scale were obtained. The discrepancies among the variants were found to be large: global annual run‐off varied by 1·9 times; its surface and sub‐surface components by 2·2 and 1·8 times, respectively; run‐off ratio by 1·5 times and the other hydrological quantities by 1·2–1·3 times. Uncertainties in precipitation datasets translate to uncertainties in run‐off and evapotranspiration depending on the ratio of real evapotranspiration to its potential value E/E0. In the areas with high E/E0, differences in P across precipitation datasets mainly translated to differences in R. In the areas with the lowest values of E/E0, run‐off is nearly insensitive to changes in P, while uncertainty in P mostly translates to uncertainty in E. Uncertainties in E and R due to different radiation datasets are the same in absolute units. Impact of uncertainties in soil parameters on estimations of water balance components can be comparable with the impact of uncertainties in forcing data. Copyright © 2010 John Wiley & Sons, Ltd.

Evaluation of water resources in a high-mountain basin in Serra da Estrela, Central Portugal, using a semi-distributed hydrological model

High-mountain basins provide a source of valuable water resources. This paper presents hydrological models for the evaluation of water resources in the high-mountain Zezere river basin in Serra da Estrela, Central Portugal. Models are solved with VISUAL BALAN v2.0, a code which performs daily water balances in the root zone, the unsaturated zone and the aquifer and requires a small number of parameters. A lumped hydrological model fails to fit measured stream flows. Its limitations are overcome by considering the dependence of the temperature and precipitation data with elevation and the spatial variability in hydrogeomorphological variables with nine sub-basins of uniform parameters. Model parameters are calibrated by fitting stream flow measurements in the Zezere river. Computed stream flows are highly sensitive to soil thickness, whereas computed groundwater recharge is most sensitive to the interflow and percolation recession coefficients. Interflow is the main component of total runoff, ranging from 41 to 55% of annual precipitation. High interflows are favored by the steep relief of the basin, by the presence of a high permeability soil overlying the fractured low permeability granitic bedrock and by the extensive subhorizontal fracturing at shallow depths. Mean annual groundwater recharge ranges from 11 to 15% of annual precipitation. It has a significant uncertainty due to uncertainties in soil parameters. This methodology proves to be useful to handle the research difficulties regarding a complex mountain basin in a context of data scarcity.

Site- and Motion-Dependent Parametric Uncertainty of Site-Response Analyses in Earthquake Simulations

We investigate the propagation of uncertainty in site-response analyses from the soil model parameters to the ground surface motion at three downhole array sites in the Los Angeles (LA) Basin. For this purpose, we develop realistic stochastic models of elastic and nonlinear dynamic soil properties using extensive site-specific and generic geotechnical data on the variability of soil properties. We also generate synthetic ground motions using a finite source dynamic rupture model over a wide range of magnitudes and distances and use this statistically significant number of ground motions in the analysis. For each of the three sites, we evaluate the effects of soil parameter uncertainty as a function of the seismic input intensity and frequency content. We show that the frequency range, where the ground-motion variability due to soil parameter uncertainty is maximized, is a function of both the site and the seismogram characteristics. We compare our results with previously published studies and show that different soil models, statistical descriptions of soil parameters, or ground-motion scenarios may yield substantial differences in the estimated site-response scatter. We conclude that the effects of nonlinear soil property uncertainties on the ground-motion variability strongly depend on the seismic motion intensity, and this dependency is more pronounced for soft soil profiles. By contrast, the effects of velocity profile uncertainties are less intensity dependent and more sensitive to the velocity impedance in the near surface that governs the maximum site amplification.

Reproducibility of soil moisture ensembles when representing soil parameter uncertainty using a Latin Hypercube–based approach with correlation control

Representation of model input uncertainty is critical in ensemble‐based data assimilation. Monte Carlo sampling of model inputs produces uncertainty in the hydrologic state through the model dynamics. Small Monte Carlo ensemble sizes are desirable because of model complexity and dimensionality but potentially lead to sampling errors and correspondingly poor representation of probabilistic structure of the hydrologic state. We compare two techniques to sample soil hydraulic and thermal properties (SHTPs): (1) Latin Hypercube (LH) based sampling with correlation control and (2) random sampling from SHTP marginal distributions. A hydrology model is used to project SHTP uncertainty onto the soil moisture state for given forcings. For statistical comparison, we generate 20 ensembles for 7 ensemble sizes. Variance in ensemble moment estimates decreases with increasing ensemble size. The LH‐based approach yields less variance in the estimate of ensemble moments at all ensemble sizes, an advantage greatest with small ensembles. Implications for hydrologic uncertainty assessment, data assimilation, and parameter estimation are discussed.

Effect of soil parameter uncertainty on seismic demand of low-rise steel buildings on dense silty sand

Focusing on low-rise steel buildings supported by shallow isolated foundations on dense silty sand, this study demonstrates the effect of uncertainty in soil parameters on seismic response of structures. Considering a set of 20 ground motions representing 10% in 50 years hazard level and concentrating on peak base moment, base shear and interstory drift as the demand variables of interest, it is found that uncertainty in soil parameters may result in significant response variability of the structures, especially when vertical factor of safety is low and the structure is relatively stiff. Uncertainty in friction angle results in significant variability of the peak base moment and base shear, while peak interstory drift ratio is found to be virtually unaffected by uncertainty in soil parameters. It is also found that a linear soil–structure-interaction (SSI) model will not be able to predict such response variability under these set of ground motions.

Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions

Abstract. A project established at the National Institute of Water and Atmospheric Research (NIWA) in New Zealand is aimed at developing a prototype of a real-time landslide forecasting system. The objective is to predict temporal changes in landslide probability for shallow, rainfall-triggered landslides, based on quantitative weather forecasts from numerical weather prediction models. Global weather forecasts from the United Kingdom Met Office (MO) Numerical Weather Prediction model (NWP) are coupled with a regional data assimilating NWP model (New Zealand Limited Area Model, NZLAM) to forecast atmospheric variables such as precipitation and temperature up to 48 h ahead for all of New Zealand. The weather forecasts are fed into a hydrologic model to predict development of soil moisture and groundwater levels. The forecasted catchment-scale patterns in soil moisture and soil saturation are then downscaled using topographic indices to predict soil moisture status at the local scale, and an infinite slope stability model is applied to determine the triggering soil water threshold at a local scale. The model uses uncertainty of soil parameters to produce probabilistic forecasts of spatio-temporal landslide occurrence 48~h ahead. The system was evaluated for a damaging landslide event in New Zealand. Comparison with landslide densities estimated from satellite imagery resulted in hit rates of 70–90%.