New Structure-Based Model for Estimating Undrained Shear Strength

This paper presents an experimental study of the strength in anisotropic clays by means of centrifuge model, cone penetration, and vane shear tests. To understand the effects of void ratio, overconsolidation ratio, and testing rate on the undrained shear strength (Su) of anisotropic Speswhite clay, a new centrifugal testing technique is designed to obtain constant overconsolidation ratio (OCR) profiles with varying void ratios (e), called the “descending gravity test.” The parameters controlling the generation of peak shear strength are quantified. As a result of this function, a new material and rate-dependent surface is defined in the e-OCR-Su space, which is identified as a “structural state capacity surface” since it relates the anisotropic structure to structure inherent capacity and properties. A new function for the estimation of excess pore pressure (uex) generated by cone penetration is found. By combining the strength and pore pressure functions a new model is proposed, called the “CU model.” The CU model is a structure-based model that provides reliable estimates of shear strength for in situ saturated clays using the knowledge of void and overconsolidation ratios. Finally, by combining Su-e-OCR and uex-e-OCR relationships, it estimates the void ratio and OCR profiles of anisotropic clays from piezocone penetration test results.     

Experimental Study of Wellbore Instability in Clays

This paper presents the results of an extensive program of laboratory model wellbore tests that have been performed to study wellbore instability in saturated clays. The tests were conducted on resedimented Boston blue clay (RBBC) anisotropically consolidated to vertical effective stresses up to 10?MPa by using two custom-built thick-walled cylinder (TWC) devices with outer diameters Do = 7.6 and 15.2?cm. The experimental program investigated the effects of specimen geometry, mode of loading, strain rate, consolidation stress level, and overconsolidation ratio (OCR) on deformations of the model wellbore measured during undrained shearing. Results indicate that for normally consolidated clays most of the change in cavity pressure occurs at volumetric strains less than 5% after which the borehole becomes unstable. Increases in outer diameter and strain rate led to a reduction in the minimum borehole pressure. Stress-strain properties were interpreted by using an analysis procedure originally developed for undrained plane strain expansion of hollow cylinders. The backfigured undrained strength ratios from these analyses for normally consolidated specimens range from su/σvc′ = 0.19–0.22. Overconsolidation greatly improves the stability of the borehole, and interpreted undrained strength ratios from the TWC tests are consistent with well-known power law functions previously developed for elemental shear tests.     

Undrained Response of Clays to Varying Strain Rate

The undrained stress–deformation behavior of clays is significantly affected by the loading rate. Based on the observed experimental response, it is possible to model the strain rate response of clays as an apparent overconsolidated clay response. In this work, we have quantitatively determined the change in the overconsolidation ratio (OCR) due to a change in the strain rate. Our results show that the shear strength response and the excess pore water pressure response at the peak stress provide quantitatively similar magnitudes of change in the OCR with increasing strain rate. This finding holds true for both normally and overconsolidated clays. This suggests that the relationship between strain rate and change in the OCR can be considered as an inherent material characteristic and is quantifiable. A relationship between the change in the OCR and change in the strain rate is described.     

Constitutive Model of Soil Based on a Dynamical Systems Approach

A soil when sheared ultimately reaches a steady-state condition at which it deforms at a constant shear stress, effective normal stress, and void ratio. Various systems in nature dynamically evolve similarly from some initial condition, to a final steady-state condition. Such systems have been studied using dynamical systems theory. This technical note uses this theory to model monotonic shear of soil as a dynamical system. The principle proposed is simple—the rates of change of the shear stress, effective normal stress, and void ratio are proportional to the applied values of the shear and effective normal stress with the proportionality values decaying with strain until ultimately these proportionality values become zero at the steady-state condition. It provides a well-formed qualitative principle that fits closely the stress-strain-void ratio curves of undrained shear tests on uncemented, resedimented clays at various over consolidated ratios (OCRs), and drained shear tests on sands and silts at various relative densities, for various stress paths including compression, extension from standard triaxial, and true-triaxial tests. For the undrained shear of resedimented clay, these proportionalities and their decay rates vary smoothly with OCR. For drained shear of sand and silt, the model parameters show orderly variation with relative density. Its value lies in that a well-formed qualitative principle derived from the steady-state condition provides an alternate approach to current complex elastoplastic models based on critical state theory.     

Analysis of Factors Influencing Soil Classification Using Normalized Piezocone Tip Resistance and Pore Pressure Parameters

This paper discusses the development of a framework for classifying soil using normalized piezocone test (CPTU) data from the corrected tip resistance (qt) and penetration pore-water pressure at the shoulder (u2). Parametric studies for normalized cone tip resistance (Q = qcnet/σv0′) and normalized excess pressures (Δu2/σv0′) as a function of overconsolidation ratio (OCR = σvy′/σv0′) during undrained penetration are combined with piezocone data from clay sites, as well as results from relatively uniform thick deposits of sands, silts, and varietal clays from around the globe. The study focuses on separating the influence of yield stress ratio from that of partial consolidation on normalized CPTU parameters, which both tend to increase Q and decrease the pore pressure parameter (Bq = Δu2/qcnet). The resulting recommended classification chart is significantly different from existing charts, and implies that assessment of data in Q–Δu2/σv0′ space is superior to Q–Bq space when evaluating piezocone data for a range of soil types. Still, there are zones of overlap for silty soils and heavily overconsolidated clays, thus requiring that supplementary information to Q and Δu2/σv0′ be obtained in unfamiliar geologies, including variable rate penetration tests, dissipation tests, CPT friction ratio, or soil sampling.     

Multiple-Porosity Model for the Transport of Reactive Contaminants in Fissured Soils with Nonequilibrium Partitioning

A multiple-porosity model for the transport of reactive contaminants in fissured media with multiple-source, nonequilibrium partitioning is proposed, widening the scope of existing models. The proposed model extends the bicontinuum, dual-porosity concepts by combining five contaminant compartments: (1) mobile water in the fissures; (2) immobile water in the fissures; (3) water diffusing into the soil matrix; (4) sorption in the fissures; and (5) sorption in the soil matrix. Both instantaneous and nonequilibrium sorption are represented in the fissures. Mobile/immobile compartments, fissured soils, and nonequilibrium sorption have been hitherto treated separately or in pairs. Exchange of contaminants occurs between all compartments. Equations for the model are formulated and transformed into the Laplace domain. Solutions for the one-dimensional problem of a leaking storage tank overlying a fissured soil are found. The effects of the inclusion of various contaminant compartments and exchange parameters are analyzed through numerical experiments.     

OCR Prediction Using Support Vector Machine Based on Piezocone Data

The determination of the overconsolidation ratio (OCR) of clay deposits is an important task in geotechnical engineering practice. This paper examines the potential of a support vector machine (SVM) for predicting the OCR of clays from piezocone penetration test data. SVM is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. The five input variables used for the SVM model for prediction of OCR are the corrected cone resistance (qt), vertical total stress (σv), hydrostatic pore pressure (u0), pore pressure at the cone tip (u1), and the pore pressure just above the cone base (u2). Sensitivity analysis has been performed to investigate the relative importance of each of the input parameters. From the sensitivity analysis, it is clear that qt=primary in situ data influenced by OCR followed by σv, u0, u2, and u1. Comparison between SVM and some of the traditional interpretation methods is also presented. The results of this study have shown that the SVM approach has the potential to be a practical tool for determination of OCR.     

Artificial neural network and finite element modeling of nanoindentation tests

This study first presents two-dimensional (2-D) axisymmetric and three-dimensional (3-D) finite element (FE) models of nanoindentation tests. Calculated load-displacement curves from the FE models are compared with the load-displacement curves from nanoindentation measurements on annealed copper. Numerical parametric studies are also performed to examine the effect of indenter geometry and the material’s stress-strain behavior on the load-displacement response. The 2-D and 3-D FE load-displacement curves compare well with the measured results on annealed copper. The second aspect of this study introduces a new modeling approach for indentation tests using artificial neural networks (ANN). In this approach, ANN models are generated to approximate the FE load-displacement curves for a wide range of material and geometric parameters. The ability of the ANN models to predict the indentation response is examined against other FE results not used as part of the training data. These models are shown to accurately predict the load-displacement behavior of a nonlinear homogeneous material as well as one with a hard film, such as an oxide film, on a relatively soft substrate. It is shown that the monotonic indentation load-displacement response during loading contains ample information for the ANN model to extract material flow properties of the indented material.     

Support Vector Machines Approach to HMA Stiffness Prediction

The application of artificial intelligence (AI) techniques to engineering has increased tremendously over the last decade. Support vector machine (SVM) is one efficient AI technique based on statistical learning theory. This paper explores the SVM approach to model the mechanical behavior of hot-mix asphalt (HMA) owing to high degree of complexity and uncertainty inherent in HMA modeling. The dynamic modulus (|E?|), among HMA mechanical property parameters, not only is important for HMA pavement design but also in determining HMA pavement performance associated with pavement response. Previously employed approaches for development of the predictive |E?| models concentrated on multivariate regression analysis of database. In this paper, SVM-based |E?| prediction models were developed using the latest comprehensive |E?| database containing 7,400 data points from 346 HMA mixtures. The developed SVM models were compared with the existing multivariate regression-based |E?| model as well as the artificial neural networks (ANN) based |E?| models developed recently by the writers. The prediction performance of SVM model is better than multivariate regression-based model and comparable to the ANN. Fewer constraints in SVM compared to ANN can make it a promising alternative considering the availability of limited and nonrepresentative data frequently encountered in construction materials characterization.     

Evaluation of artificial neural network modelling to predict torso muscle activity

Owing to the complexities involved in obtaining direct measures of in vivo muscle forces, validation of predictive models of muscle activity has been difficult. An artificial neural network (ANN) model had been previously developed for the estimation of lumbar muscle activity during moderate levels of static exertion. The predictive ability of this model is evaluated in this study using several techniques, including comparison of response surfaces and composite statistical tests of values derived from model output, with multiple EMG experimental datasets. ANN-predicted activation levels were accurately modelled to within 3% across a range of experiments and levels of combined flexion/extension and lateroflexion loadings. The results indicate both a high degree of consistency in the averaged muscle activity measured in several different experiments, and substantiate the ability of the ANN model to predict generalized recruitment patterns. It also is suggested that the use of multiple comparison methods provides a better indication of model behaviour and prediction accuracy than a single evaluation criterion.     

3D Modeling of an NATM Tunnel in High K0 Clay Using Two Different Constitutive Models

This paper studies the accuracy of the three-dimensional finite-element predictions of a displacement field induced by tunneling using new Austrian tunneling method (NATM) in stiff clays with high K0 conditions. The studies are applied to the Heathrow express trial tunnel. Two different constitutive models are used to represent London Clay, namely a hypoplastic model for clays and the modified Cam-clay (MCC) model. Good quality laboratory data are used for parameter calibration and accurate field measurements are used to initialize K0 and void ratio. The hypoplastic model gives better predictions than the MCC model with satisfactory estimate for the displacement magnitude and slightly overestimated width of the surface settlement trough. Parametric studies demonstrate the influence of variation of the predicted soil behavior in the very-small-strain to large-strain range and the influence of the time dependency of the shotcrete lining behavior.     

ANN and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff

This study presents the development of artificial neural network (ANN) and fuzzy logic (FL) models for predicting event-based rainfall runoff and tests these models against the kinematic wave approximation (KWA). A three-layer feed-forward ANN was developed using the sigmoid function and the backpropagation algorithm. The FL model was developed employing the triangular fuzzy membership functions for the input and output variables. The fuzzy rules were inferred from the measured data. The measured event based rainfall-runoff peak discharge data from laboratory flume and experimental plots were satisfactorily predicted by the ANN, FL, and KWA models. Similarly, all the three models satisfactorily simulated event-based rainfall-runoff hydrographs from experimental plots with comparable error measures. ANN and FL models also satisfactorily simulated a measured hydrograph from a small watershed 8.44?km2 in area. The results provide insights into the adequacy of ANN and FL methods as well as their competitiveness against the KWA for simulating event-based rainfall-runoff processes.     

Case Study: Finite Element Method and Artificial Neural Network Models for Flow through Jeziorsko Earthfill Dam in Poland

A finite element method (FEM) and an artificial neural network (ANN) model were developed to simulate flow through Jeziorsko earthfill dam in Poland. The developed FEM is capable of simulating two-dimensional unsteady and nonuniform flow through a nonhomogenous and anisotropic saturated and unsaturated porous body of an earthfill dam. For Jeziorsko dam, the FEM model had 5,497 triangular elements and 3,010 nodes, with the FEM network being made denser in the dam body and in the neighborhood of the drainage ditches. The ANN model developed for Jeziorsko dam was a feedforward three layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning. The water levels on the upstream and downstream sides of the dam were input variables and the water levels in the piezometers were the target outputs in the ANN model. The two models were calibrated and verified using the piezometer data collected on a section of the Jeziorsko dam. The water levels computed by the models satisfactorily compared with those measured by the piezometers. The model results also revealed that the ANN model performed as good as and in some cases better than the FEM model. This case study offers insight into the adequacy of ANN as well as its competitiveness against FEM for predicting seepage through an earthfill dam body.     

Modeling Reference Evapotranspiration Using Evolutionary Neural Networks

The ability of evolutionary neural networks (ENN) to model reference evapotranspiration (ET0) was investigated in this study. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed of three stations in central California, Windsor, Oakville, and Santa Rosa, were used as inputs to the ENN models to estimate ET0 obtained using the FAO-56 Penman-Monteith equation. In the first part of the study, a comparison was made between the estimates provided by the ENN and those of the following empirical models: the California Irrigation Management System, Penman, Hargreaves, modified Hargreaves, and Ritchie methods. Root-mean-squared error, coefficient of efficiency, and correlation coefficient statistics were used as comparing criteria for the evaluation of the models’ accuracies. The ENN performed better than the empirical models. In the second part of the study, the ENN results were compared with those of the conventional artificial neural networks (ANN). The comparison results revealed that the ENN models were superior to ANN in modeling the ET0 process.     

Adaptive Neurofuzzy Computing Technique for Evapotranspiration Estimation

The accuracy of an adaptive neurofuzzy computing technique in estimation of reference evapotranspiration (ET0) is investigated in this paper. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from two stations, Pomona and Santa Monica, in Los Angeles, Calif., are used as inputs to the neurofuzzy model to estimate ET0 obtained using the FAO-56 Penman–Monteith equation. In the first part of the study, a comparison is made between the estimates provided by the neurofuzzy model and those of the following empirical models: The California Irrigation Management System, Penman, Hargreaves, and Ritchie. In this part of the study, the empirical models are calibrated using the standard FAO-56 PM ET0 values. The estimates of the neurofuzzy technique are also compared with those of the calibrated empirical models and artificial neural network (ANN) technique. Mean-squared errors, mean-absolute errors, and determination coefficient statistics are used as comparing criteria for the evaluation of the models’ performances. The comparison results reveal that the neurofuzzy models could be employed successfully in modeling the ET0 process. In the second part of the study, the potential of the neurofuzzy technique, ANN and the empirical methods in estimation ET0 using nearby station data are investigated.     

Influence of delayed viral production on viral dynamics in HIV-1 infected patients

We present and analyze a model for the interaction of human immunodeficiency virus type 1 (HIV-1) with target cells that includes a time delay between initial infection and the formation of productively infected cells. Assuming that the variation among cells with respect to this 'intracellular' delay can be approximated by a gamma distribution, a high flexible distribution that can mimic a variety of biologically plausible delays, we provide analytical solutions for the expected decline in plasma virus concentration after the initiation of antiretroviral therapy with one or more protease inhibitors. We then use the model to investigate whether the parameters that characterize viral dynamics can be identified from biological data. Using non-linear least-squares regression to fit the model to simulated data in which the delays conform to a gamma distribution, we show that good estimates for free viral clearance rates, infected cell death rates, and parameters characterizing the gamma distribution can be obtained. For simulated data sets in which the delays were generated using other biologically plausible distributions, reasonably good estimates for viral clearance rates, infected cell death rates, and mean delay times can be obtained using the gamma-delay model. For simulated data sets that include added simulated noise, viral clearance rate estimates are not as reliable. If the mean intracellular delay is known, however, we show that reasonable estimates for the viral clearance rate can be obtained by taking the harmonic mean of viral clearance rate estimates from a group of patients. These results demonstrate that it is possible to incorporate distributed intracellular delays into existing models for HIV dynamics and to use these refined models to estimate the half-life of free virus from data on the decline in HIV-1 RNA following treatment.     

Multiple-Porosity Contaminant Transport by Finite-Element Method

An exponential finite-element model for multiple-porosity contaminant transport in soils is proposed. The model combines three compartments for dissolved contaminants: a primary compartment of diffusion–advection transport with nonequilibrium sorption, a secondary compartment with diffusion in rectangular or spherical soil blocks, and a tertiary compartment for immobile solutions within the primary compartment. Hence the finite-element model can be used to solve four types of mass-transfer problems which include: (1) intact soils, (2) intact soils with multiple sources of nonequilibrium partitioning, (3) soils with a network of regularly spaced fissures, and (4) structured soils. Hitherto, mobile/immobile compartments, fissured soils, and nonequilibrium sorption have been treated separately or in pairs. A Laplace transform is applied to the governing equations to remove the time derivative. A Galerkin residual statement is written and a finite-element method is developed. Both polynomial and exponential finite elements are implemented. The solution is inverted to the time domain numerically. The method is validated by comparison to analytical and boundary element predictions. Exponential elements perform particularly well, speeding up convergence significantly. The scope of the method is illustrated by analyzing contamination from a set of four waste repositories buried in fissured clay.     

Effects of Disturbance on Undrained Strengths Interpreted from Pressuremeter Tests

Although the cylindrical cavity expansion theory should provide a sound basis for obtaining the undrained shear strength of clays from pressuremeter tests, the interpreted strengths are often inconsistent with data measured in high-quality laboratory tests. This paper investigates how the pressuremeter results are affected by disturbances that inevitably occur during device installation. The installation of self-boring and displacement-type pressuremeters is simulated using strain path analyses, with realistic effective stress-strain-strength properties described by the MIT-E3 model. Derived strengths obtained from the simulated expansion of displacement-type pressuremeters tend to underestimate the in situ∕cavity expansion strength by amounts that depend on the relative volume of soil displaced, the time delay prior to testing, and the initial overconsolidation ratio of the clay. Interpretation procedures using the simulated contraction curves give much more reliable estimates of the true undrained shear strength. The simulated disturbance effects of self boring lead to derived peak shear stresses that are significantly higher than the reference undrained shear strengths. This overestimate depends on the volume of soil removed during installation and is enhanced when the finite membrane length is included in the analyses. Self-boring pressuremeter data from a well-documented test site in Boston confirm the general character of the predicted pressuremeter stress-strain behavior. The theoretical analyses underestimate the peak strengths derived from self-boring pressuremeter (SBPM) expansion tests, but match closely the measured postpeak resistance in the strain range of 3–6% (saddle point condition). Saddle point strengths are similar in magnitude to the shear strengths measured in laboratory undrained triaxial compression tests at this site. The current predictions are not able to explain the very high shear strengths derived from the SBPM contraction curves.     

Spatial Distribution of Excess Pore-Water Pressure due to Piezocone Penetration in Overconsolidated Clay

This paper presents the results of an analysis of the spatial distribution of the excess pore-water pressure induced by piezocone penetration into overconsolidated clays. From the experimental results obtained for moderately and heavily overconsolidated clays, it was observed that the excess pore-water pressure increases monotonically from the piezocone surface to the outer boundary of the shear zone and then decreases logarithmically, approaching zero at the outer boundary of the plastic zone. It was also found that the size of the shear zone decreases from approximately 2.2 to 1.5 times the cone radius with increasing overconsolidation ratio (OCR), whereas the plastic radius is about 11 times the piezocone radius, regardless of the OCR. The expressions developed in this study based on the modified Cam clay model and the cylindrical cavity expansion theory, which take into consideration the effects of the strain rate and stress anisotropy, provide a good prediction of the initial pore-water pressure at the piezocone location. The method of predicting the spatial distribution of excess pore-water pressure proposed in this study is based on a linearly increasing Δushear in the shear zone and a logarithmically decreasing Δuoct, and was verified by comparing the pore-water pressure measured in overconsolidated specimens in the calibration chamber.     

Hybrid Neural Network—Finite Element River Flow Model

Results obtained from a hybrid neural network—finite element model are reported in this paper. The hybrid model incorporates artificial neural network (ANN) nodes into a numerical scheme, which solves the two-dimensional shallow water equations using finite elements (FE). First, numerical computations are carried out on the entire numerical model, using a larger mesh. The results from this computation are then used to train several preselected ANN nodes. The ANN nodes model the response for a part of the entire numerical model by transferring the system reaction to the location where both models are connected in real time. This allows a smaller mesh to be used in the hybrid ANN-FE model, resulting in savings in computation time. The hybrid model was developed for a river application, using the computational nodes located at the open boundaries to be the ANN nodes for the ANN-FE hybrid model. Real-time coupling between the ANN and FE models was achieved, and a reduction is CPU time of more than 25% was obtained.