scholarly journals Model Uncertainty for Settlement Prediction on Axially Loaded Piles in Hydraulic Fill Built in Marine Environment

2021 ◽  
Vol 9 (1) ◽  
pp. 63
Author(s):  
Manuel Bueno Aguado ◽  
Félix Escolano Sánchez ◽  
Eugenio Sanz Pérez
Keyword(s):  
Model Uncertainty ◽  
Numerical Models ◽  
Probabilistic Approach ◽  
Prediction Method ◽  
Soil Conditions ◽  
Hydraulic Fill ◽  
Wide Range ◽  
Engineering Problems ◽  
Axially Loaded ◽  
Pile Deformation

Model uncertainty is present in many engineering problems but particularly in those involving geotechnical behavior of pile foundation. A wide range of soil conditions together with simplified numerical models makes it a constant necessity to review the accuracy of the predictions. In this paper, the outputs of some seventy (70) pile axially-loaded tests have been reviewed with a classic numerical model to assess pile deformation. Probabilistic approach has been used to quantify uncertainties coming from soil tests, statistic uncertainty and also from the model itself. In this way, a critical review of the prediction method and a way to quantify its uncertainty is presented. The method is intended to be used in a wide range of engineering problems.

scholarly journals Assessing the perturbations of the hydrogeological regime in sloping fens due to roads

2020 ◽  
Vol 24 (1) ◽  
pp. 213-226
Author(s):  
Fabien Cochand ◽  
Daniel Käser ◽  
Philippe Grosvernier ◽  
Daniel Hunkeler ◽  
Philip Brunner
Keyword(s):  
Numerical Models ◽  
Subsurface Flow ◽  
Flow Dynamics ◽  
Qualitative Assessment ◽  
Soil Conditions ◽  
Systematic Analysis ◽  
The Road ◽  
Wide Range ◽  
The Impact ◽  
Surface And Subsurface Flow

Abstract. Roads in sloping fens constitute a hydraulic barrier for surface and subsurface flow. This can lead to the drying out of downslope areas of the sloping fen as well as gully erosion. Different types of road construction have been proposed to limit the negative implications of roads on flow dynamics. However, so far, no systematic analysis of their effectiveness has been carried out. This study presents an assessment of the hydrogeological impact of three types of road structures in semi-alpine, sloping fens in Switzerland. Our analysis is based on a combination of field measurements and fully integrated, physically based modeling. In the field approach, the influence of roads was examined using tracer tests in which the area upslope of the road was sprinkled with a saline solution. The spatial distribution of electrical conductivity downslope provided a qualitative assessment of the flow paths and, thus, the implications of the road structures on subsurface flow. A quantitative albeit not site-specific assessment was carried out using fully coupled numerical models jointly simulating surface and subsurface flow processes. The different road types were implemented and their influence on flow dynamics was assessed for a wide range of slopes and different hydraulic conductivities of the soil. The models are based on homogenous soil conditions, allowing for a relative ranking of the impact of the road types. For all cases analyzed in the field and simulated using the numerical models, roads designed with an L drain (i.e., collecting water upslope and releasing it in a concentrated manner downslope) constitute the largest perturbations in terms of flow dynamics. The other road structures investigated were found to have less impact. The developed methodologies and results can be used for the planning of future road projects in sloping fens.

System Dynamics Modeling of Porous Media

1997 ◽  
Vol 119 (2) ◽  
pp. 251-259 ◽  
Author(s):  
E. P. Fahrenthold ◽  
M. Venkataraman
Keyword(s):  
Porous Media ◽  
Reference Frames ◽  
Numerical Models ◽  
Bond Graph ◽  
Dynamics Modeling ◽  
Element Discretization ◽  
Highly Nonlinear ◽  
Wide Range ◽  
Engineering Problems ◽  
Bond Graph Theory

A wide range of engineering problems involve porous media modeling. General porous media models are highly nonlinear, geometrically complex, and must account for energy transfer between fluid and solid constituents normally modeled in distinct Lagrangian and Eulerian reference frames. Combining finite element discretization techniques with bond graph methods greatly simplifies the model formulation process, as compared to alternative schemes based on weighted residual solutions of the governing partial differential equations. The result generalizes existing numerical models of porous media and current network thermodynamics/bond graph theory.

scholarly journals Two-Dimensional Numerical Modeling of Flow in Physical Models of Rock Vane and Bendway Weir Configurations

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 458
Author(s):  
Drew C. Baird ◽  
Benjamin Abban ◽  
S. Michael Scurlock ◽  
Steven B. Abt ◽  
Christopher I. Thornton
Keyword(s):  
Numerical Modeling ◽  
Physical Model ◽  
Numerical Models ◽  
Hydraulic Performance ◽  
Physical Models ◽  
Protection Measures ◽  
Design Engineers ◽  
Model Study ◽  
Study Results ◽  
Wide Range

While there are a wide range of design recommendations for using rock vanes and bendway weirs as streambank protection measures, no comprehensive, standard approach is currently available for design engineers to evaluate their hydraulic performance before construction. This study investigates using 2D numerical modeling as an option for predicting the hydraulic performance of rock vane and bendway weir structure designs for streambank protection. We used the Sedimentation and River Hydraulics (SRH)-2D depth-averaged numerical model to simulate flows around rock vane and bendway weir installations that were previously examined as part of a physical model study and that had water surface elevation and velocity observations. Overall, SRH-2D predicted the same general flow patterns as the physical model, but over- and underpredicted the flow velocity in some areas. These over- and underpredictions could be primarily attributed to the assumption of negligible vertical velocities. Nonetheless, the point differences between the predicted and observed velocities generally ranged from 15 to 25%, with some exceptions. The results showed that 2D numerical models could provide adequate insight into the hydraulic performance of rock vanes and bendway weirs. Accordingly, design guidance and implications of the study results are presented for design engineers.

scholarly journals A Computational Approach to Solve a System of Transcendental Equations with Multi-Functions and Multi-Variables

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 920
Author(s):  
Chukwuma Ogbonnaya ◽  
Chamil Abeykoon ◽  
Adel Nasser ◽  
Ali Turan
Keyword(s):  
Numerical Models ◽  
Problem Formulation ◽  
Science And Engineering ◽  
Physical Systems ◽  
Engineering Problems ◽  
Parametric Studies ◽  
Independent Variable ◽  
Transcendental Equations ◽  
The Waves ◽  
Modelling Approach

A system of transcendental equations (SoTE) is a set of simultaneous equations containing at least a transcendental function. Solutions involving transcendental equations are often problematic, particularly in the form of a system of equations. This challenge has limited the number of equations, with inter-related multi-functions and multi-variables, often included in the mathematical modelling of physical systems during problem formulation. Here, we presented detailed steps for using a code-based modelling approach for solving SoTEs that may be encountered in science and engineering problems. A SoTE comprising six functions, including Sine-Gordon wave functions, was used to illustrate the steps. Parametric studies were performed to visualize how a change in the variables affected the superposition of the waves as the independent variable varies from x1 = 1:0.0005:100 to x1 = 1:5:100. The application of the proposed approach in modelling and simulation of photovoltaic and thermophotovoltaic systems were also highlighted. Overall, solutions to SoTEs present new opportunities for including more functions and variables in numerical models of systems, which will ultimately lead to a more robust representation of physical systems.

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

scholarly journals Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Viscosity Data ◽  
Oil Viscosity ◽  
Pvt Data ◽  
Proposed Model ◽  
Wide Range ◽  
Engineering Problems ◽  
Functional Forms ◽  
Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

Dynamical Evolution of Globular Clusters with Mass Spectrum

1991 ◽  
Vol 9 (1) ◽  
pp. 41-44
Author(s):  
Hyung Mok Lee
Keyword(s):  
Mass Spectrum ◽  
Globular Clusters ◽  
Numerical Models ◽  
Relaxation Times ◽  
Dynamical Evolution ◽  
Planck Equation ◽  
Mass Function ◽  
Low Mass Stars ◽  
Wide Range ◽  
Three Body

AbstractWe present a series of numerical models describing the dynamical evolution of globular clusters with a mass spectrum, based on integration of the Fokker-Planck equation. We include three-body binary heating and a steady galactic tidal field. A wide range of initial mass functions is adopted and the evolution of the mass function is examined. The mass function begins to change appreciably during the post-collapse expansion phase due to the selective evaporation of low mass stars through the tidal boundary. One signature of highly evolved clusters is thus the significant flattening of the mass function. The age (in units of the half-mass relaxation time) increases very rapidly beyond about 100 signifying the final stage of cluster disruption. This appears to be consistent with the sharp cut-off of half-mass relaxation times at near 108 years for the Galactic globular clusters.

Coupled Thermo-Hydro-Geochemical Models of Engineered Barrier Systems: The Febex Project

2000 ◽  
Vol 663 ◽  
Author(s):  
J. Samper ◽  
R. Juncosa ◽  
V. Navarro ◽  
J. Delgado ◽  
L. Montenegro ◽  
...  
Keyword(s):  
Large Scale ◽  
Reference Model ◽  
Numerical Models ◽  
Full Scale ◽  
Small Scale ◽  
Model Parameters ◽  
In Situ Test ◽  
Wide Range ◽  
Engineered Barrier

ABSTRACTFEBEX (Full-scale Engineered Barrier EXperiment) is a demonstration and research project dealing with the bentonite engineered barrier designed for sealing and containment of waste in a high level radioactive waste repository (HLWR). It includes two main experiments: an situ full-scale test performed at Grimsel (GTS) and a mock-up test operating since February 1997 at CIEMAT facilities in Madrid (Spain) [1,2,3]. One of the objectives of FEBEX is the development and testing of conceptual and numerical models for the thermal, hydrodynamic, and geochemical (THG) processes expected to take place in engineered clay barriers. A significant improvement in coupled THG modeling of the clay barrier has been achieved both in terms of a better understanding of THG processes and more sophisticated THG computer codes. The ability of these models to reproduce the observed THG patterns in a wide range of THG conditions enhances the confidence in their prediction capabilities. Numerical THG models of heating and hydration experiments performed on small-scale lab cells provide excellent results for temperatures, water inflow and final water content in the cells [3]. Calculated concentrations at the end of the experiments reproduce most of the patterns of measured data. In general, the fit of concentrations of dissolved species is better than that of exchanged cations. These models were later used to simulate the evolution of the large-scale experiments (in situ and mock-up). Some thermo-hydrodynamic hypotheses and bentonite parameters were slightly revised during TH calibration of the mock-up test. The results of the reference model reproduce simultaneously the observed water inflows and bentonite temperatures and relative humidities. Although the model is highly sensitive to one-at-a-time variations in model parameters, the possibility of parameter combinations leading to similar fits cannot be precluded. The TH model of the “in situ” test is based on the same bentonite TH parameters and assumptions as for the “mock-up” test. Granite parameters were slightly modified during the calibration process in order to reproduce the observed thermal and hydrodynamic evolution. The reference model captures properly relative humidities and temperatures in the bentonite [3]. It also reproduces the observed spatial distribution of water pressures and temperatures in the granite. Once calibrated the TH aspects of the model, predictions of the THG evolution of both tests were performed. Data from the dismantling of the in situ test, which is planned for the summer of 2001, will provide a unique opportunity to test and validate current THG models of the EBS.

Multiscale unified prediction of size/scale and Hall-Petch effects in the mechanics of polycrystalline materials

2013 ◽  
Vol 22 (1-2) ◽  
pp. 67-71 ◽  
Author(s):  
George N. Frantziskonis
Keyword(s):  
Size Effects ◽  
Probabilistic Approach ◽  
Higher Dimensions ◽  
Spatial Dimension ◽  
Polycrystalline Materials ◽  
Material Failure ◽  
Average Grain Size ◽  
Wide Range ◽  
Physical Information ◽  
Polycrystalline Solids

AbstractMaterials show size effects in their strength, i.e., improved strength as size decreases. Size effects have been studied extensively at a wide range of scales, from atomistic to continuum. Size effects depend on the scale of reference, as the physics change with increasing or decreasing scale. The work reported herein concentrates at scales near the average grain size in polycrystalline solids, where they are examined in conjunction with Hall-Petch effects. It presents a process for isolating physical information on a problem at specific spatial or temporal scales and applies it to Hall-Petch and size effects in one spatial dimension, extendable to higher dimensions. Importantly, the scale-isolated information captures the interactions among scales. As material failure and Hall-Petch effects are highly stochastic, a probabilistic approach to the present work is more appropriate than a deterministic one.

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