Simulations of Bending Experiments of Concrete Beams by Stochastic Discrete Model

2014 ◽  
Vol 627 ◽  
pp. 457-460
Author(s):  
Jana Kaděrová ◽  
Jan Eliáš
Keyword(s):  
Experimental Data ◽  
Material Properties ◽  
Discrete Model ◽  
Concrete Beams ◽  
Particle Model ◽  
Model Parameters ◽  
Three Point Bending ◽  
Model Response ◽  
Different Types ◽  
Lattice Particle

The paper describes results of numerical simulations of experiments on concrete beams loaded in three-point bending. Stochastic lattice-particle model has been applied in which the material was represented by discrete particles of random size and location. Additional spatial variability of material properties was introduced by stationary autocorrelated random field. Three different types of geometrically similar beams were modeled: half-notched, fifth-notched and unnotched, each in four different sizes. The deterministic and stochastic model parameters were identified via automatic procedure based on comparison to a subset of experimental data, so that the adequacy of the model response could be validated by comparison with the remaining experimental data.

scholarly journals BAYESIAN INFERENCE OF HETEROGENEOUS VISCOPLASTIC MATERIAL PARAMETERS

2018 ◽  
Vol 15 ◽  
pp. 41-45
Author(s):  
Eliška Janouchová ◽  
Anna Kučerová
Keyword(s):  
Experimental Data ◽  
Bayesian Inference ◽  
Material Model ◽  
Isotropic Hardening ◽  
Model Parameters ◽  
Viscoplastic Material ◽  
Loading Test ◽  
Model Response ◽  
Probabilistic Description ◽  
Probabilistic Setting

<p>Modelling of heterogeneous materials based on randomness of model input parameters involves parameter identification which is focused on solving a stochastic inversion problem. It can be formulated as a search for probabilistic description of model parameters providing the distribution of the model response corresponding to the distribution of the observed data</p><p>In this contribution, a numerical model of kinematic and isotropic hardening for viscoplastic material is calibrated on a basis of experimental data from a cyclic loading test at a high temperature. Five material model parameters are identified in probabilistic setting. The core of the identification method is the Bayesian inference of uncertain statistical moments of a prescribed joint lognormal distribution of the parameters. At first, synthetic experimental data are used to verify the identification procedure, then the real experimental data are processed to calibrate the material model of copper alloy.</p>

scholarly journals Damage Detection of Steel-Concrete Composite Beam

2018 ◽  
Vol 28 (3) ◽  
pp. 30-49
Author(s):  
Maciej Szumigała ◽  
Agnieszka Pełka-Sawenko ◽  
Tomasz Wróblewski ◽  
Małgorzata Abramowicz
Keyword(s):  
Experimental Data ◽  
Damage Detection ◽  
Discrete Model ◽  
Composite Beams ◽  
Model Parameters ◽  
Discrete Wavelet ◽  
Identification Procedure ◽  
Original Algorithm ◽  
Automated Optimization ◽  
Made In

Abstract The paper presents analysis results of steel-concrete composite beams, identification and attempts to detect damage introduced in a discrete model. Analysis of damage detection was conducted using DDL (Damage, Detection, Localization), our own original algorithm. Changes of dynamic and static parameters of the model were analysed in damage detection. Discrete wavelet transform was used for damage localization in the model. Prior to ultimate analysis, two-tier identification of discrete model parameters based on experimental data was made. In identification procedure, computational software (Python, Abaqus, Matlab) was connected in automated optimization loops. Results positively verified the original DDL algorithm for damage detection in steel-concrete composite beams, which enables further analysis using experimental data.

scholarly journals Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage

Metals ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 876 ◽  
Author(s):  
Ehsan Adeli ◽  
Bojana Rosić ◽  
Hermann G. Matthies ◽  
Sven Reinstädler ◽  
Dieter Dinkler
Keyword(s):  
Experimental Data ◽  
Parameter Identification ◽  
Measurement Errors ◽  
Surface Displacement ◽  
Life Time ◽  
Material Model ◽  
Model Parameters ◽  
Bayesian Approaches ◽  
Model Response ◽  
The Difference

The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore, identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the focus is on the identification of material parameters of a viscoplastic damaging material using a stochastic simulation technique to generate artificial data which exhibit the same stochastic behavior as experimental data. It is proposed to use Bayesian inverse methods for parameter identification and therefore the model and damage parameters are identified by applying the Transitional Markov Chain Monte Carlo Method (TMCMC) and Gauss-Markov-Kalman filter (GMKF) approach. Identified parameters by using these two Bayesian approaches are compared with the true parameters in the simulation and with each other, and the efficiency of the identification methods is discussed. The aim of this study is to observe which one of the mentioned methods is more suitable and efficient to identify the model and damage parameters of a material model, as a highly non-linear model, using a limited surface displacement measurement vector and see how much information is indeed needed to estimate the parameters accurately.

scholarly journals Online Estimation of Terminal Airspace Sector Capacity from ATC Workload

2021 ◽  
Author(s):  
Majeed Mohamed
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Experimental Data ◽  
Neural Model ◽  
Model Parameters ◽  
Air Traffic Controller ◽  
Single Output ◽  
Estimated Parameters ◽  
Model Response ◽  
Relative Standard

Neural Partial Differentiation (NPD) approach is applied to estimate terminal airspace sector capacity in real-time from the ATC (Air Traffic Controller) dynamical neural model with permissible safe separation and affordable workload. A neural model of a multi-input-single-output (MISO) ATC dynamical system is primarily established and used to estimate parameters from the experimental data using NPD. Since the relative standard deviations of these estimated parameters are lesser, the predicted neural model response is well matched with the intervention of ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters that are unknown in practice.

Bayesian Updating of Aleatory Uncertainties in Heterogeneous Materials

2017 ◽  
Vol 1144 ◽  
pp. 136-141
Author(s):  
Eliška Janouchová ◽  
Anna Kučerová ◽  
Jan Sýkora
Keyword(s):  
Bayesian Inference ◽  
Probability Distribution ◽  
Parameter Identification ◽  
Material Properties ◽  
Model Parameters ◽  
Experimental Measurements ◽  
Identification Procedure ◽  
Model Response ◽  
Probabilistic Description ◽  
Probabilistic Setting

Advances in meta-modelling and increasing computational capacity of modern computerspermitted many researches to focus on parameter identification in probabilistic setting. Increasinglypopular Bayesian inference allows to estimate model parameters together with corresponding epistemicuncertainties from indirect experimental measurements. However in case of a heterogeneousmaterial model, the identification procedure has to be able to quantify the aleatory uncertainties capturingthe variability of the material properties. Parameter identification of a heterogeneous materialmodel can be formulated as a search for probabilistic description of its parameters providing the distributionof the model response corresponding to the distribution of the observed data, i.e. a stochasticinversion problem. By prescribing a specific type of probability distribution to the model parameterswith corresponding uncertain moments, the task changes to the identification of these so-calledhyperparameters of the distribution which can be inferred in the Bayesian way.

scholarly journals A Hyper-Viscoelastic Continuum-Level Finite Element Model of the Spinal Cord Assessed for Transverse Indentation and Impact Loading

2021 ◽  
Vol 9 ◽  
Author(s):  
Aleksander Rycman ◽  
Stewart McLachlin ◽  
Duane S. Cronin
Keyword(s):  
Experimental Data ◽  
Spinal Cord ◽  
Finite Element ◽  
Impact Loading ◽  
Material Properties ◽  
Injury Risk ◽  
Model Parameters ◽  
Pia Mater ◽  
Level Model ◽  
Linear Material

Finite Element (FE) modelling of spinal cord response to impact can provide unique insights into the neural tissue response and injury risk potential. Yet, contemporary human body models (HBMs) used to examine injury risk and prevention across a wide range of impact scenarios often lack detailed integration of the spinal cord and surrounding tissues. The integration of a spinal cord in contemporary HBMs has been limited by the need for a continuum-level model owing to the relatively large element size required to be compatible with HBM, and the requirement for model development based on published material properties and validation using relevant non-linear material data. The goals of this study were to develop and assess non-linear material model parameters for the spinal cord parenchyma and pia mater, and incorporate these models into a continuum-level model of the spinal cord with a mesh size conducive to integration in HBM. First, hyper-viscoelastic material properties based on tissue-level mechanical test data for the spinal cord and hyperelastic material properties for the pia mater were determined. Secondly, the constitutive models were integrated in a spinal cord segment FE model validated against independent experimental data representing transverse compression of the spinal cord-pia mater complex (SCP) under quasi-static indentation and dynamic impact loading. The constitutive model parameters were fit to a quasi-linear viscoelastic model with an Ogden hyperelastic function, and then verified using single element test cases corresponding to the experimental strain rates for the spinal cord (0.32–77.22 s−1) and pia mater (0.05 s−1). Validation of the spinal cord model was then performed by re-creating, in an explicit FE code, two independent ex-vivo experimental setups: 1) transverse indentation of a porcine spinal cord-pia mater complex and 2) dynamic transverse impact of a bovine SCP. The indentation model accurately matched the experimental results up to 60% compression of the SCP, while the impact model predicted the loading phase and the maximum deformation (within 7%) of the SCP experimental data. This study quantified the important biomechanical contribution of the pia mater tissue during spinal cord deformation. The validated material models established in this study can be implemented in computational HBM.

Parameter Determination for Chloride and Tritium Transport in Undisturbed Lysimeters during Steady Flow

1992 ◽  
Vol 23 (2) ◽  
pp. 89-104 ◽  
Author(s):  
Ole H. Jacobsen ◽  
Feike J. Leij ◽  
Martinus Th. van Genuchten
Keyword(s):  
Experimental Data ◽  
Unsaturated Flow ◽  
Transport Model ◽  
Time Moment ◽  
Breakthrough Curves ◽  
Coarse Sand ◽  
Retardation Factor ◽  
Parameter Determination ◽  
Model Parameters ◽  
Water Fraction

Breakthrough curves of Cl and 3H2O were obtained during steady unsaturated flow in five lysimeters containing an undisturbed coarse sand (Orthic Haplohumod). The experimental data were analyzed in terms of the classical two-parameter convection-dispersion equation and a four-parameter two-region type physical nonequilibrium solute transport model. Model parameters were obtained by both curve fitting and time moment analysis. The four-parameter model provided a much better fit to the data for three soil columns, but performed only slightly better for the two remaining columns. The retardation factor for Cl was about 10 % less than for 3H2O, indicating some anion exclusion. For the four-parameter model the average immobile water fraction was 0.14 and the Peclet numbers of the mobile region varied between 50 and 200. Time moments analysis proved to be a useful tool for quantifying the break through curve (BTC) although the moments were found to be sensitive to experimental scattering in the measured data at larger times. Also, fitted parameters described the experimental data better than moment generated parameter values.

scholarly journals Modelling the Inflation and Elastic Instabilities of Rubber-Like Spherical and Cylindrical Shells Using a New Generalised Neo-Hookean Strain Energy Function

2021 ◽  
Author(s):  
Afshin Anssari-Benam ◽  
Andrea Bucchi ◽  
Giuseppe Saccomandi
Keyword(s):  
Experimental Data ◽  
Energy Function ◽  
Limit Point ◽  
Strain Energy ◽  
Cylindrical Shells ◽  
Strain Energy Function ◽  
Model Parameters ◽  
Wide Range ◽  
Cylindrical Tubes ◽  
Gent Model

AbstractThe application of a newly proposed generalised neo-Hookean strain energy function to the inflation of incompressible rubber-like spherical and cylindrical shells is demonstrated in this paper. The pressure ($P$ P ) – inflation ($\lambda $ λ or $v$ v ) relationships are derived and presented for four shells: thin- and thick-walled spherical balloons, and thin- and thick-walled cylindrical tubes. Characteristics of the inflation curves predicted by the model for the four considered shells are analysed and the critical values of the model parameters for exhibiting the limit-point instability are established. The application of the model to extant experimental datasets procured from studies across 19th to 21st century will be demonstrated, showing favourable agreement between the model and the experimental data. The capability of the model to capture the two characteristic instability phenomena in the inflation of rubber-like materials, namely the limit-point and inflation-jump instabilities, will be made evident from both the theoretical analysis and curve-fitting approaches presented in this study. A comparison with the predictions of the Gent model for the considered data is also demonstrated and is shown that our presented model provides improved fits. Given the simplicity of the model, its ability to fit a wide range of experimental data and capture both limit-point and inflation-jump instabilities, we propose the application of our model to the inflation of rubber-like materials.

scholarly journals Developments in Modelling Bone Screwing

2020 ◽  
Vol 6 (3) ◽  
pp. 111-114
Author(s):  
Jack Wilkie ◽  
Paul D. Docherty ◽  
Knut Möller
Keyword(s):  
Material Properties ◽  
Frictional Coefficient ◽  
Viscous Friction ◽  
Model Parameters ◽  
General Shape ◽  
Bone Material ◽  
Bone Material Properties ◽  
Modelling Methodology ◽  
Recorded Data ◽  
Friction Models

AbstractINTRODUCTION: A torque-rotation model of the bone-screwing process has been proposed. Identification of model parameters using recorded data could potentially be used to determine the material properties of bone. These properties can then be used to recommend tightening torques to avoid over or under-tightening of bone screws. This paper improves an existing model to formulate it in terms of material properties and remove some assumptions. METHOD: The modelling methodology considers a critical torque, which is required to overcome friction and advance the screw into the bone. Below this torque the screw may rotate with elastic deformation of the bone tissue, and above this the screw moves relative to the bone, and the speed is governed by a speed-torque model of the operator’s hand. The model is formulated in terms of elastic modulus, ultimite tensile strength, and frictional coefficient of the bone and the geometry of the screw and hole. RESULTS: The model output shows the speed decreasing and torque increasing as the screw advances into the bone, due to increasing resistance. The general shape of the torque and speed follow the input effort. Compared with the existing model, this model removes the assumption of viscous friction, models the increase in friction as the screw advances into the bone, and is directly in terms of the bone material properties. CONCLUSION: The model presented makes significant improvements on the existing model. However it is intended for use in parameter identification, which was not evaluated here. Further simulation and experimental validation is required to establish the accuracy and fitness of this model for identifying bone material properties.

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