Random Field Modeling of Elastic Properties Using Homogenization

This paper addresses the random field characterization of elastic properties. Consistent random field properties of a continuum are derived from a simple stochastic micromechanical model of the continuum. Existing displacement-based techniques to homogenize the discrete microstructure are discussed and a new force-based technique is introduced. The stochastic features of a micromechanically consistent continuum can be considerably different from those based on a straightforward randomization of deterministic constitutive laws. Examples indicate the relevance for structural reliability. The impact of uncertainties associated with selecting micromechanical models is assessed.     

Probabilistic Assessment of Stress Normalization for CPT Data

Currently available cone penetration test (CPT) stress normalization schemes exhibit no consensus on the estimation of the stress normalization component. Depending on which power law stress normalization exponent is used, very different interpretations may result in the analyses where normalized CPT data are used (e.g., CPT-based soil classification and seismic soil liquefaction initiation assessment). Within the confines of this paper, it is intended to clarify and resolve some of these differences, and to propose improved recommendations for CPT stress normalization. For this purpose, available stress normalization databases from theoretical, numerical, and field data analyses approaches were compiled. For the soil types, and stress conditions where compiled database is not conclusive, additional finite element simulations have been performed. The resulting relationship not only eliminates several sources of bias intrinsic to previous, similar correlations, and provides greatly reduced overall uncertainty and variance, it also helps to establish a consensus to the stress normalization issue that have long been difficult and controversial. Key elements in the development of these new correlations are: (1) accumulation of a significantly expanded database of analytical/numerical CPT simulation results, as well as field and chamber test data from homogeneous soil layers; (2) use of improved knowledge and understanding of factors affecting CPT and stress normalization; and (3) use of high-order probabilistic tools (Bayesian updating).     

Sensitivity of Field Data Estimates in One-Dimensional Hydraulic Modeling of Channels

A hydraulic simulation study was carried out for Red Hill Creek, an urban stream channel in Ontario, Canada. Over one million simulations were conducted using the HEC-RAS4b model to evaluate the sensitivity of model predictions to field data accuracy, density and estimation techniques and provide guidance toward balancing human resource allocation with model accuracy. Increased cross-sectional discretization and improved estimates of floodplain roughness dominate the accuracy of the results of computed water surface elevations. For a range of field hours available for data collection, a sampling strategy focused on maximizing the number of sparsely detailed cross sections is shown to outperform a sampling strategy using the same number of field hours to sample fewer cross sections with a higher resolution.     

Importance of Field Data in Stream Water Quality Modeling Using QUAL2E-UNCAS

The Stream Water Quality Model QUAL2E-UNCAS is widely used to simulate the dissolved oxygen of streams under steady flow conditions. It is the latest version of a series of water quality models that have a long history in systems analysis in water quality management, and has been applied to a number of streams and rivers around the world. This paper summarizes the conceptual representation of the computer model, briefly reviews a number of applications of the model that have been published in open literature, describes the included uncertainty analysis capability, and discusses the importance of field data in model predictions. Experience with the QUAL2E model has proven the importance of site-specific data to model predictions. An accurate representation of the properties of the system significantly contributes to simulation success.     

Prediction of Soil Composition from CPT Data Using General Regression Neural Network

Soil type is typically inferred from the information collected during a cone penetration test (CPT) using one of the many available soil classification methods. In this study, a general regression neural network (GRNN) was developed for predicting soil composition from CPT data. Measured values of cone resistance and sleeve friction obtained from CPT soundings, together with grain-size distribution results of soil samples retrieved from adjacent standard penetration test boreholes, were used to train and test the network. The trained GRNN model was tested by presenting it with new, previously unseen CPT data, and the model predictions were compared with the reference particle-size distribution and the results of two existing CPT soil classification methods. The profiles of soil composition estimated by the GRNN generally compare very well with the actual grain-size distribution profiles, and overall the neural network had an 86% success rate at classifying soils as coarse grained or fine grained.     

Logistic Regression Model for Evaluating Soil Liquefaction Probability Using CPT Data

A simple model for evaluating liquefaction probability using cone penetration test (CPT) data is developed based on logistic regression analyses of 396 case histories. The proposed model uses the normalized cone penetration resistance and soil behavior type index as input parameters; therefore, only CPT testing is necessary for evaluating the liquefaction probability of a site. The selection of the model parameters and the expression of equations are based on results of probability examinations and rigorous statistical analyses. Moreover, the derivation of the logistic regression model is presented in a system of equations. The incorporation of these procedures in developing the model not only fully satisfies the statistic requirements but also highlights the physical meanings of the model parameters. Comparisons of the proposed probability model with previously proposed deterministic and probabilistic approaches are performed to demonstrate the improvements. For practical purposes, the developed model is implemented to establish the relationship between the factor of safety against liquefaction and the probability of liquefaction.     

Two-Dimensional Random Field of Mechanical Soil Properties

In situ tests were performed and samples were taken from a uniform layer of clay used as a material for the production of bricks. The shear resistance was measured, using Geonor Inspection Vane Tester H-60, in a quadratic mesh of 961 test points in the horizontal plane, spaced at 15 cm. Vane resistance tests were also performed in the laboratory on 76 undisturbed samples, on which the constrained modulus in consolidometers was determined. The aim of the research was to find the parameters of the 2D random field of shear strength and the correlation of this field with that of constrained modulus. The strong correlation obtained eventually enabled estimating the autocorrelation function of the constrained modulus describing the elastic behavior of the soil. A simple estimation procedure is proposed, enabling curve fitting of different isotropic and anisotropic correlation functions. Additionally, some statistical analyses of data were performed, including regression and time series analysis. The problem of the local and global variabilities of soil properties and their ramifications is also considered.     

Simulation of Multidimensional Binary Random Fields with Application to Modeling of Two-Phase Random Media

This paper proposes a methodology for simulation of binary random fields with application to the problem of generating sample realizations of two-phase random media. The methodology is based on the concept of nonlinear transformations with memory of Gaussian random fields. The simulation is performed according to the autocorrelation function of the binary field which contains considerable information about the microstructural characteristics of the medium. The determination of the probabilistic characteristics of the underlying Gaussian field is achieved through an iterative procedure that was introduced in a previous paper by the same authors in one dimension and is extended here to multiple dimensions. Limiting cases and alternative mappings are also presented. The capabilities of the methodology are demonstrated in a series of examples.     

Soil Salinity Sampling Strategy Using Spatial Modeling Techniques, Remote Sensing, and Field Data

The aim of this study was to develop a methodology for collecting soil salinity samples. The objectives of this paper are to: (1) estimate the number of soil salinity samples needed to capture the variability in the soil salinity data with high accuracy; and (2) compare two types of satellite images with different resolutions: Ikonos with 4?m resolution and Landsat 5 with 30?m resolution. To achieve these objectives, two satellite images were acquired (one for Ikonos and one for Landsat 5) to evaluate the correlation between the measured soil salinity and remote sensing data. From the observed data, three subsets were randomly extracted with each subset containing: 75, 50, and 25% of the data for each field. These three subsets were then used in the modeling process. Ordinary least squares (OLS) (i.e., multiple regression) was used to explore the coarse-scale variability in soil salinity as a function of the Ikonos and Landsat 5 bands. A stepwise procedure was used to identify the best subset of satellite bands to include in the regression models that minimized the Akakie information criteria (AIC). Then, the spatial structure of the residuals from the OLS models were described using sample variogram models. The variogram model with the smallest AIC was selected to describe the spatial dependencies in the soil salinity data. If the residuals were spatially correlated, ordinary kriging was used to model the spatial distribution of soil salinity in the fields. A tenfold cross validation was used to estimate the prediction error for soil salinity. To evaluate the effectiveness of the models, various measures of the prediction error were computed. This study provides an accurate methodology that can be used by researchers in reducing the number of soil samples that need to be collected. This is especially valuable in projects that last several years. The results of this study suggest that the number of soil samples that need to be collected and therefore their cost can be significantly reduced and soil salinity estimation can be significantly improved by using kriging. The results also show that the Ikonos image performed better than the Landsat 5 image.     

Random Field Representation in a Biorthogonal Wavelet Basis

This paper develops a new representation scheme for random fields, which can be used in a variety of mechanics applications, based upon the projection onto a biorthogonal wavelet basis. The merits of such a scheme can be shown to result from the relaxation of the condition of orthonormality, while still requiring compact support of the basis functions. Earlier methods have relied on the diagonalization properties of wavelets to demonstrate how the Daubechies family of orthonormal wavelets is effective in weakening the correlation across scales for a large class of random processes. It is shown that biorthogonal processes achieve better decorrelation owing to the fact that fewer filter coefficients are needed to maintain the same support of basis functions when compared to the Daubechies family. Numerical examples of fields encountered in earthquake engineering and other applications are given.     

Empirically Based Gamma-Distributed Random Wall Pressure Field in Silo

Measurements show that silo wall pressures exhibit large fluctuations in time and space during discharge of the silo. This observation is important for the design of the silo wall because spatial pressure variations may impose substantial bending moments in the silo wall that otherwise may be small or vanishing due to the carrying ability of the membrane forces in the silo wall. Information about the stochastic properties of this pressure variation cannot be obtained from any existing continuum model for the silo medium flowing within the confinement of the silo. Therefore the modeling must presently be tied to statistical analyses of the empirical evidence combined with simple mechanical principles. This paper analyzes the same data set as that analyzed by Munch-Andersen et al., but on the basis of a completely different and physically less disputable stochastic pressure field model than the one considered by them. It is shown that an explicitly constructed gamma distribution type of field in equilibrium with itself fits well to the measurements made in the Swedish Karpalund silo.     

Water Modeling of Optimizing Tundish Flow Field

 In the water modeling experiments, three cases were considered, ie, a bare tundish, a tundish equipped with a turbulence inhibitor, and a rectangular tundish equipped with weirs (dams) and a turbulence inhibitor. Comparing the RTD curves, inclusion separation, and the result of the streamline experiment, it can be found that the tundish equipped with weirs (dams) and a turbulence inhibitor has a great effect on the flow field and the inclusion separation when compared with the sole use or no use of the turbulent inhibitor or weirs (dams). In addition, the enlargement of the distance between the weir and dam will result in a better effect when the tundish equipped with weirs (dam) and a turbulence inhibitor was used.     

Numerical Modeling of River Flow for Ecohydraulic Applications: Some Experiences with Velocity Characterization in Field and Simulated Data

Information regarding the spatial and temporal organization of river flow is required for many applications in river management, and is a fundamental requirement in ecohydraulics. As an alternative to detailed field surveys and to mesohabitat reconnaissance schemes, potential exists to deploy numerical flow simulation as an assessment and design tool. A key question is the extent to which complex hydrodynamic models are really practical in river management applications. This paper presents experiences using sediment simulation in intakes with multiblock, a three-dimensional modeling code, in conjunction with a statistical approach for classifying the spatiotemporal dynamics of flow behavior. Even in a simple configuration, the model is able to replicate well flow structures which associate with the mesohabitat concepts used in field reconnaissance techniques. The model also captures spatiotemporal dynamics in flow and depth behavior at these scales. However, because the model shows differential performance between flow stages and between differing channel (bed form) units, the smaller-scale and discharge-dependent dynamics of some zones within the channel may be less-well represented, and the implications of this for future research are noted.     

Viscoelastic Modeling and Field Validation of Flexible Pavements

The objective of this study was to characterize hot-mix asphalt (HMA) viscoelastic properties at intermediate and high temperatures and to incorporate laboratory-determined parameters into a three-dimensional finite element (FE) model to accurately simulate pavement responses to vehicular loading at different temperatures and speeds. Results of the developed FE model were compared against field-measured pavement responses from the Virginia Smart Road. Results of this analysis indicated that the elastic theory grossly underpredicts pavement responses to vehicular loading at intermediate and high temperatures. In addition, the elastic FE model could not simulate permanent deformation or delayed recovery, a known characteristic of HMA materials. In contrast, results of the FE viscoelastic model were in better agreement with field measurements. In this case, the average error in the prediction was less than 15%. The FE model successfully simulated retardation of the response in the transverse direction and rapid relaxation of HMA in the longitudinal direction. Moreover, the developed model allowed predicting primary rutting damage at the surface and its partial recovery after load application.     

高温等离子体电磁场速度场温度场的模拟

采用涡量 -流函数法和κ -ε双方程湍流模型对不同工况条件下的高温等离子体的电磁场、速度场与温度场进行了数值模拟 ,从而对高温等离子体传输过程的规律有了全面的了解 ,为采用高温等离子体为热源的工业炉及设备的设计和最佳工艺操作提供了理论依据。     

USGS Streamflow Data and Modeling Sand-Bed Rivers

United States Geological Survey streamflow data are commonly used for hydraulic model calibration and boundary conditions. The transitory nature of sand-bed rivers’ bathymetry is problematic for the traditional automated stream gauging methods used by the USGS. This note seeks to assess the limitations of streamflow measurements for use in hydraulic models. An overview of USGS rating-curve development and use is presented with a focus on the specific challenges of sand-bed rivers. Measurements from three consecutive USGS gauges for a storm event on the Rio Grande in Albuquerque, New Mexico, illustrate the outlined problems with rating curves. These gauges are utilized to study the impact of uncertainty in rating-curve discharges on hydraulic model results. A one dimensional hydraulic model of the study reach indicates up to 25% reduction in the calculated flow depth if questionable rating-curve discharges are used as model input. Recommendations for using USGS streamflow data in hydraulic models are outlined.     

Numerical Modeling of Three-Dimensional Flow Field Around Circular Piers

A three-dimensional numerical model FLUENT is used to simulate the separated turbulent flow around vertical circular piers in clear water. Computations are performed using different turbulence models and results are compared with several sets of experimental data available in the literature. Despite commonly perceived weakness of the k-ε model in resolving three-dimensional (3D) open channel and geophysical flows, several variants of this turbulence model are found to have performed satisfactorily in reproducing the measured velocity profiles. However, model results obtained using the k-ε models show some discrepancy with the measured bed shear stress. The Reynolds stress model performed quite well in simulating velocity distribution on flat bed and scour hole as well as shear stress distribution on flat bed around circular piers. The study demonstrates that a robust 3D hydrodynamic model can effectively supplement experimental studies in understanding the complex flow field and the scour initiation process around piers of various size, shape, and dimension.