scholarly journals Determination of GTN Model Parameters Based on Artificial Neutral Network for a Ductile Failure

2021 ◽  
Vol 2 (1) ◽  
pp. 01-05
Author(s):  
YASSINE CHAHBOUB ◽  
SZAVAI Szabolcs
Keyword(s):  
Ductile Failure ◽  
Compact Tension ◽  
Model Parameters ◽  
Single Edge ◽  
Direct Identification ◽  
Simulation Data ◽  
Short Period ◽  
Artificial Neural Network Ann ◽  
Excellent Tool

The Gurson – Tvergaard – Needleman (GTN) mechanical model is widely used to predict the failure of materials based on laboratory specimens, direct identification of Gurson – Tvergaard – Needleman parameters is not easy and time-consuming, and the most used method to determine them is the combination between the experimental results and those of the finite elements, the process consists of repeating the simulations several times until the simulation data matches the experimental data obtained at the specimen level.This article aims to find GTN parameters for the Compact Tension (CT) and Single Edge Tensile Test (SENT) specimen based on the Notch Specimen (NT) using the Artificial Neural Network (ANN) approach. . This work presents how the ANN could help us determine the parameters of GTN in a very short period of time. The results obtained show that ANN is an excellent tool for determining GTN parameters.

scholarly journals Time-domain determination of earthquake fault parameters from short-period P-waves

1976 ◽  
Vol 66 (5) ◽  
pp. 1459-1484
Author(s):  
Paul G. Somerville ◽  
Ralph A. Wiggins ◽  
Robert M. Ellis
Keyword(s):  
Time Domain ◽  
Time Function ◽  
Wave Radiation ◽  
Model Parameters ◽  
Half Cycle ◽  
Rupture Velocity ◽  
Fault Surface ◽  
Fault Parameters ◽  
Short Period

abstract Source parameters of two shallow earthquakes have been determined by the time-domain analysis of short-period teleseismic recordings. For each event, the effect of the receiver crust was deconvolved from a set of globally distributed recordings using the homomorphic method. The resulting seismograms were compared with the form of the elastic-wave radiation computed from Savage's model of radially spreading rupture on a plane elliptical fault surface. This time-domain approach has permitted the determination of several kinematic parameters pertaining to the dynamics of rupture that are not ordinarily evaluated from spectral analysis. These parameters are rupture velocity, the direction of farthest rupture propagation, and the duration of a ramp dislocation time function which was prescribed to be the same everywhere on the fault surface. The application of a general linear inverse scheme has shown that the model parameters (notably rupture velocity and dimension) are only weakly coupled. Inversion is also used to determine the range of acceptable parameter values and indicates the importance of array recordings in constraining the models. A consistent discrepancy between the observed and model seismograms during the first half-cycle of motion is attributed to the incorrect prescription of the dislocation time function. It is suggested that a space-dependent function determined theoretically by Kostrov in 1964 would tend to remove this discrepancy.

Lateral Displacement of Liquefaction Induced Ground Using Least Square Support Vector Machine

2011 ◽  
Vol 2 (2) ◽  
pp. 29-39 ◽  
Author(s):  
Sarat Kumar Das ◽  
Pijush Samui ◽  
Dookie Kim ◽  
N. Sivakugan ◽  
Rajanikanta Biswal
Keyword(s):  
Support Vector Machine ◽  
Model Equation ◽  
Lateral Displacement ◽  
Least Square ◽  
Disaster Mitigation ◽  
Support Vector ◽  
Model Parameters ◽  
Ann Models ◽  
Artificial Neural Network Ann

The determination of lateral displacement of liquefaction induced ground during an earthquake is an imperative task in disaster mitigation. This study investigates the possibility of using least square support vector machine (LSSVM) for the prediction of lateral displacement of liquefaction induced ground during an earthquake. The results have been compared with those obtained using artificial neural network (ANN) models and observed that LSSVM outperformed the ANN models. Model equation has been presented based on the model parameters, which can be used by the professionals. Sensitivity analysis has also been performed to determine the importance of each of the input parameters.

Experience with the IMMa tyre test bench for the determination of tyre model parameters using genetic techniques

2005 ◽  
Vol 43 (sup1) ◽  
pp. 253-266 ◽  
Author(s):  
J. A. Cabrera ◽  
A. Ortiz ◽  
E. Carabias ◽  
A. Simón
Keyword(s):  
Test Bench ◽  
Model Parameters ◽  
Tyre Model ◽  
Genetic Techniques

scholarly journals Machine Learning Approaches for Prediction of the Compressive Strength of Alkali Activated Termite Mound Soil

2021 ◽  
Vol 11 (11) ◽  
pp. 4754
Author(s):  
Assia Aboubakar Mahamat ◽  
Moussa Mahamat Boukar ◽  
Nurudeen Mahmud Ibrahim ◽  
Tido Tiwa Stanislas ◽  
Numfor Linda Bih ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Construction Materials ◽  
Curing Temperature ◽  
Sustainable Construction ◽  
Support Vector ◽  
Learning Approaches ◽  
Artificial Neural Network Ann ◽  
Curing Regime ◽  
Alkali Activated

Earth-based materials have shown promise in the development of ecofriendly and sustainable construction materials. However, their unconventional usage in the construction field makes the estimation of their properties difficult and inaccurate. Often, the determination of their properties is conducted based on a conventional materials procedure. Hence, there is inaccuracy in understanding the properties of the unconventional materials. To obtain more accurate properties, a support vector machine (SVM), artificial neural network (ANN) and linear regression (LR) were used to predict the compressive strength of the alkali-activated termite soil. In this study, factors such as activator concentration, Si/Al, initial curing temperature, water absorption, weight and curing regime were used as input parameters due to their significant effect in the compressive strength. The experimental results depict that SVM outperforms ANN and LR in terms of R2 score and root mean square error (RMSE).

scholarly journals Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 766
Author(s):  
Rashad A. R. Bantan ◽  
Ramadan A. Zeineldin ◽  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Research Study ◽  
Real Data ◽  
Factors Affecting ◽  
King Abdulaziz University ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.

PO-0220 Determination of dose deposition from an Ocular Brachytherapy source: simulation data with TOPAS

2021 ◽  
Vol 158 ◽  
pp. S182-S183
Author(s):  
I. Knoll ◽  
L. de Souza ◽  
P. Ramon ◽  
A. Quevedo ◽  
T. Alves Pianoschi Alva ◽  
...  
Keyword(s):  
Simulation Data ◽  
Dose Deposition ◽  
Brachytherapy Source

scholarly journals The seven sisters DANCe

2018 ◽  
Vol 612 ◽  
pp. A70 ◽  
Author(s):  
J. Olivares ◽  
E. Moraux ◽  
L. M. Sarro ◽  
H. Bouy ◽  
A. Berihuete ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Model Comparison ◽  
Precise Determination ◽  
Model Parameters ◽  
Data Sets ◽  
Adequate Model ◽  
Comparison Results ◽  
Bayesian Evidence ◽  
Mass Segregation

Context. Membership analyses of the DANCe and Tycho + DANCe data sets provide the largest and least contaminated sample of Pleiades candidate members to date. Aims. We aim at reassessing the different proposals for the number surface density of the Pleiades in the light of the new and most complete list of candidate members, and inferring the parameters of the most adequate model. Methods. We compute the Bayesian evidence and Bayes Factors for variations of the classical radial models. These include elliptical symmetry, and luminosity segregation. As a by-product of the model comparison, we obtain posterior distributions for each set of model parameters. Results. We find that the model comparison results depend on the spatial extent of the region used for the analysis. For a circle of 11.5 parsecs around the cluster centre (the most homogeneous and complete region), we find no compelling reason to abandon King’s model, although the Generalised King model introduced here has slightly better fitting properties. Furthermore, we find strong evidence against radially symmetric models when compared to the elliptic extensions. Finally, we find that including mass segregation in the form of luminosity segregation in the J band is strongly supported in all our models. Conclusions. We have put the question of the projected spatial distribution of the Pleiades cluster on a solid probabilistic framework, and inferred its properties using the most exhaustive and least contaminated list of Pleiades candidate members available to date. Our results suggest however that this sample may still lack about 20% of the expected number of cluster members. Therefore, this study should be revised when the completeness and homogeneity of the data can be extended beyond the 11.5 parsecs limit. Such a study will allow for more precise determination of the Pleiades spatial distribution, its tidal radius, ellipticity, number of objects and total mass.

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