• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.21 no.3
• /
• pp.155-165
• /
• 2017

The Hansel-Spittel constitutive model requires a total of nine parameters for flow stress prediction. Typically, the parameters are estimated by least squares methods for given tensile test measurements from a deterministic perspective. In this research we took a different approach, a probabilistic viewpoint, to see through the development of the Hansel-Spittel constitutive model. This perspective change showed that deterministic least squares methods are closely related to statistical maximum likelihood methods via Gaussian noise assumption. More intriguingly, this perspective shift revealed that the Hansel-Spittel constitutive model may leave out deterministic trends in residuals despite nearly perfect agreement with measurements. With tensile test measurements of AA1070 aluminum alloy, we demonstrated this deficiency of the Hansel-Spittel constitutive model, suggesting room for improvement.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.20 no.4
• /
• pp.520-527
• /
• 2017

In this study, the experimental methods are compared for obtaining the parameters of the Johnson-Cook constitutive model. The parameters used for numerical simulation are very important in making an accurate estimation of numerical simulation. So, the testing method of obtaining the parameters is also very important. We compared the difference of conventional method, compression method and tensile method of AISI-4340 steel at various strain rate by using MTS, SHPB and SHTB. Taylor impact test and M&S were carried out to compare differences among these three types of JC constitutive parameter.

• Journal of the Korean Geotechnical Society
• /
• v.20 no.2
• /
• pp.125-130
• /
• 2004

In this paper, the method of parameter estimation of a mathematical constitutive model blown as the smooth elasto-plastic cap model is studied. To predict the response of the real soil using this model, the eight parameters describing the constitutive equations have to be determined. First, experimental data are obtained from simple laboratory experiments such as one dimensional confined compression test in a consolidometer and drained triaxial compression test with the Ottawa sand f3r the reference value. Then, the numerical experiments are performed in the cap model with initial guessed parameters. The optimization method is utilized to fit the model response to experimental data by minimizing the error between the laboratory and numerical responses. Special attention is given to the parameter estimation procedure of numerical triaxial test due to the difficulty of the lateral strain measurements.

• International Journal of Concrete Structures and Materials
• /
• v.6 no.2
• /
• pp.101-110
• /
• 2012

This paper focuses on the finite element (FE) response sensitivity and reliability analyses considering smooth constitutive material models. A reinforced concrete frame is modeled for FE sensitivity analysis followed by direct differentiation method under both static and dynamic load cases. Later, the reliability analysis is performed to predict the seismic behavior of the frame. Displacement sensitivity discontinuities are observed along the pseudo-time axis using non-smooth concrete and reinforcing steel model under quasi-static loading. However, the smooth materials show continuity in response sensitivity at elastic to plastic transition points. The normalized sensitivity results are also used to measure the relative importance of the material parameters on the structural responses. In FE reliability analysis, the influence of smoothness behavior of reinforcing steel is carefully noticed. More efficient and reasonable reliability estimation can be achieved by using smooth material model compare with bilinear material constitutive model.

• Geomechanics and Engineering
• /
• v.10 no.1
• /
• pp.37-48
• /
• 2016

The hyperbolic stress-strain model has been shown to be valid for modeling nonlinear stress-strain behavior for rockfill materials. The Duncan-Chang nonlinear constitutive model was adopted to characterize the behavior of the modeled rockfill materials in this study. Accurately estimating the model parameters of rockfill materials is a key problem for simulating dam deformations during both the dam construction period and the dam operation period. In order to estimate model parameters, triaxial compression experiments of rockfill materials were performed. Based on a genetic algorithm, the constitutive model parameters of the rockfill material were determined from the triaxial compression experimental data. The investigation results show that the predicted strains provide satisfactory precision when compared with the observed strains and the strains forecasted by a gradient-based optimization algorithm. The effectiveness of the proposed inversion procedure of model parameters was verified by experimental investigation in a laboratory.

• Journal of Industrial Technology
• /
• v.22 no.A
• /
• pp.229-240
• /
• 2002

This paper is an experimental and numerical work of Investigating the bearing capacity of shallow foundation of rubble mound under eccentric loads. Parametric centrifuge model tests at the 50g level environments with the model footings in the form of strip footing were performed by changing the loading location of model footing, relative density and materials for ground foundation. For the model ground, crushed rock sampled from a rocky mountain was prepared with a grain size distribution of having an identical coefficient of uniformity to the field condition. Model ground was also prepared with relative densities of 50 % and 80 %. For loading condition, model tests with and without eccentric load were carned out to investigate the effect of eccentric loads and a numerical analysis with the commertially available software of FLAC was performed. For numerical estimation with FLAC, the hyperbolic model of a nonlinear elastic constitutive relationship was used to simulate the stress-stram constitutive relationship of model ground and a series of triaxial compression test were carried out to find the parameters for this model Test results were analyzed and compared with Meyerhof method (1963), effective area method based on the limit equilibrium method, and a numerical analysis with FLAC.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.32 no.9
• /
• pp.740-753
• /
• 2008

The conventional Split Hopkinson Pressure Bar (C-SHPB) technique with aluminum pressure bars to achieve a closer impedance match between the pressure bars and the specimen materials such as hot temperature degraded POM (Poly Oxy Methylene) and PP (Poly Propylene) to obtain more distinguishable experimental signals is used to obtain a dynamic behavior of material deformation under a high strain rate loading condition. An experimental modification with Pulse shaper is introduced to reduce the nonequilibrium on the dynamic material response during a short test period to increase the rise time of the incident pulse for two polymeric materials. For the dynamic stress strain curve obtained from SHPB experiment under high strain rate, the Johnson-Cook model is applied as a constitutive equation, and we verify the applicability of this constitutive equation to the probabilistic reliability estimation method. The methodology to estimate the reliability using the probabilistic method such as the FORM and the SORM has been proposed, after compose the limit state function using Johnson-Cook model. It is found that the failure probability estimated by using the SORM is more reliable than those of the FORM, and the failure probability increases with the increase of applied stress. Moreover, it is noted that the parameters of Johnson-Cook model such as A and n, and applied stress affect the failure probability more than the other random variables according to the sensitivity analysis.

• Steel and Composite Structures
• /
• v.19 no.6
• /
• pp.1467-1481
• /
• 2015

This paper presents a contribution to improving an analytical thermo-mechanical modeling of Oxley's machining theory of orthogonal metals cutting, which objective is the prediction of the cutting forces, the average stresses, temperatures and the geometric quantities in primary and secondary shear zones. These parameters will then be injected into the developed model of Karas et al. (2013) to predict temperature distributions at the tool-chip-workpiece interface. The amendment to Oxley's modified model is the reduction of the estimation of time-related variables cutting process such as cutting forces, temperatures in primary and secondary shear zones and geometric variables by the introduction the constitutive equation of Johnson-Cook model. The model-modified validation is performed by comparing some experimental results with the predictions for machining of 0.38% carbon steel.

• Transactions of Materials Processing
• /
• v.27 no.1
• /
• pp.28-36
• /
• 2018

Electromagnetic forming is a type of high-speed forming process to deform a workpiece through a Lorentz force. As the high strain rate in an electromagnetic-forming simulation causes infeasibility in determining constitutive parameters, we employed inverse parameter estimation in the previous study. However, the inverse parameter estimation process required us to spend considerable time, which leads to an increase in computational cost. To overcome the computational obstacle, in this research, we applied two types of surrogate modeling methods and compared them to each other to evaluate which model is best for the electromagnetic-forming simulation. We exploited an artificial neural network and we reduced-order modeling methods. During the construction of a reduced-order model, we extracted orthogonal bases with proper orthogonal decomposition and predicted basis coefficients by utilizing an artificial neural network. After the construction of the surrogate models, we verified the artificial neural network and reduced-order models through training and testing samples. As a result, we determined the artificial neural network model is slightly more accurate than the reduced-order model. However, the construction of the artificial neural network model requires a considerably larger amount of time than that of the reduced-order model. Thus, a reduced order modeling method is more efficient than an artificial neural network for estimating the electromagnetic forming and for the rapid approximation of structural simulations which needs repetitive runs.

• Geomechanics and Engineering
• /
• v.34 no.5
• /
• pp.507-527
• /
• 2023

Accurately estimation of the geo-mechanical parameters in Artificial Ground Freezing (AGF) is a most important scientific topic in soil improvement and geotechnical engineering. In order for this, one way is using classical and conventional constitutive models based on different theories like critical state theory, Hooke's law, and so on, which are time-consuming, costly, and troublous. The others are the application of artificial intelligence (AI) techniques to predict considered parameters and behaviors accurately. This study presents a comprehensive data-mining-based model for predicting the Young's Modulus of frozen sand under the triaxial test. For this aim, several single and hybrid models were considered including additive regression, bagging, M5-Rules, M5P, random forests (RF), support vector regression (SVR), locally weighted linear (LWL), gaussian process regression (GPR), and multi-layered perceptron neural network (MLP). In the present study, cell pressure, strain rate, temperature, time, and strain were considered as the input variables, where the Young's Modulus was recognized as target. The results showed that all selected single and hybrid predicting models have acceptable agreement with measured experimental results. Especially, hybrid Additive Regression-Gaussian Process Regression and Bagging-Gaussian Process Regression have the best accuracy based on Model performance assessment criteria.