Parameter Calibration Procedure Research Articles

Recurrent Neural Networks and Three-Point Bending Test on the Identification of Material Hardening Parameters

The continuous evolution of metallic alloys in the automotive industry has led to the development of more advanced and flexible constitutive models that attempt to accurately describe the various fundamental properties and behavior of these materials. These models have become increasingly complex, incorporating a larger number of parameters that require an accurate calibration procedure to fit the constitutive parameters with experimental data. In this context, machine learning (ML) methodologies have the potential to advance material constitutive modeling, enhancing the efficiency of the material parameter calibration procedure. Recurrent neural networks (RNNs) stand out among various learning algorithms due to their ability to process sequential data and overcome limitations imposed by nonlinearities and multiple parameters involved in phenomenological models. This study explores the modeling capabilities of long short-term memory (LSTM) structures, a type of RNN, in predicting the hardening behavior of a sheet metal material using the results of a standardized experimental three-point bending test, with the aim of extending this methodology to other experimental tests and constitutive models. Additionally, a variable analysis is performed to select the most important variables for this experimental test and assess the influence of friction, material thickness, and elastic and plastic properties on the accuracy of predictions made by neural networks. The required data for designing and training the network solutions are collected from numerical simulations using finite element methodology (FEM), which are subsequently validated by experiments. The results demonstrate that the proposed LSTM-based approach outperforms traditional identification techniques in predicting the material hardening parameters. This suggests that the developed procedure can be effectively applied to efficiently characterize different materials, especially those extensively used in industrial applications, ranging from mild steels to advanced high-strength steels.

Evaluation of automatic versus material test-based calibrations of concrete models for ballistic impact simulations

Concrete is used for protective structures all over the world. Accurate response estimates to a given threat is vital for designing such structures. Concrete models often require numerous input parameters for which sufficient experimental data can be challenging to obtain. Some models are accompanied by parameter generators which use the unconfined compression strength to extrapolate the remainder of the parameters based on experimental databases. This study investigates simulation of ballistic impact on high-strength concrete with 75 MPa nominal unconfined cylindrical compressive strength. The first objective is to investigate the accuracy parameter generators to produce input data for commonly used concrete material models. The second objective is to establish and evaluate a simplified parameter calibration procedure based on standard material experiments and data from the literature. The results employing parameter generators varied notably between the models while still giving decent ballpark estimates. The parameters obtained from inverse modelling of standardized material tests improved the results significantly. The findings of this study recommend caution when using automatic parameter generators. Although a detailed calibration of these concrete models is complicated, a simplified calibration gives reasonable predictions, making this the advisable approach for designing concrete protective structures.

A mathematical-mechanical hybrid driven approach for determining the deformation monitoring indexes of concrete dam

This paper aims at quantifying the uncertainty of mechanical parameters of the concrete dam based on prototype measured data, and puts forward a novel method for determining deformation monitoring indexes based on the quantification results of the uncertainty of deformation causes. Firstly, the deformation components are separated by the HST model for deformation of concrete dam. Secondly, on the basis of the separated hydraulic component and numerical simulation, the posterior distribution of main mechanical parameters involved in the finite element model (FEM) is updated by utilizing Markov chain Monte Carlo sampling method under Bayesian framework. Meanwhile, in order to improve the computational efficiency of the parameter calibration procedure, this study adopts a surrogate model based on the multi-layer perceptron algorithm to replace the finite element calculation. The training set of the surrogate model is generated by Latin hypercube sampling in the sample space and the optimum sample size is discussed. Thirdly, based on the probability inversion analysis results of mechanical parameters, the uncertainty quantification of hydraulic component is realized by forward analysis based on FEM. The uncertainty of temperature and aging component is characterized by confidence interval method. Then, the deformation monitoring indexes are established by integrating the uncertainty quantization results of three components. Finally, the feasibility of the proposed method is verified by using the long-term deformation monitoring data of Jinping-I arch dam. The results show that the monitoring indexes determined by this method is more sensitive to the abnormal measurement due to the uncertainty quantification of hydraulic component.

A novel joint element parameter calibration procedure for the combined finite-discrete element method

The combined finite–discrete element method (FDEM) has been widely used to simulate the fracture process of rock materials. The FDEM simulation requires many material parameters which should be calibrated through laboratory tests or directly cited from previous conclusions. However, it is difficult to obtain the joint element parameters, including the joint element penalty Pf and the type I and type II fracture energies GI and GII. Therefore, a novel calibration method for the above three parameters must be proposed. First, Pf can be obtained by uniaxial compression simulation, and Pf = 30E (E is Young’s modulus) is applicable for homogeneous and isotropic rock materials. Second, an accurate GI value in the static loading state can be obtained by Brazilian disc simulation with a central preprepared failure path. Third, an accurate GII value can be calibrated by the modified variable-angle shear model. Finally, the GI and GII values with different element sizes can be calculated by the proposed linear equations. In addition, normal uniaxial and triaxial compression simulations can be used to verify the reliability of the calibrated GI and GII values. Moreover, the tunnel excavation simulation results show that the joint element parameters calibrated by the modified method can achieve cross-scale simulation, and the simulation results are more accurate.

NEAR: An artifact removal pipeline for human newborn EEG data

Electroencephalography (EEG) is arising as a valuable method to investigate neurocognitive functions shortly after birth. However, obtaining high-quality EEG data from human newborn recordings is challenging. Compared to adults and older infants, datasets are typically much shorter due to newborns’ limited attentional span and much noisier due to non-stereotyped artifacts mainly caused by uncontrollable movements. We propose Newborn EEG Artifact Removal (NEAR), a pipeline for EEG artifact removal designed explicitly for human newborns. NEAR is based on two key steps: 1) A novel bad channel detection tool based on the Local Outlier Factor (LOF), a robust outlier detection algorithm; 2) A parameter calibration procedure for adapting to newborn EEG data the algorithm Artifacts Subspace Reconstruction (ASR), developed for artifact removal in mobile adult EEG. Tests on simulated data showed that NEAR outperforms existing methods in removing representative newborn non-stereotypical artifacts. NEAR was validated on two developmental populations (newborns and 9-month-old infants) recorded with two different experimental designs (frequency-tagging and ERP). Results show that NEAR artifact removal successfully reproduces established EEG responses from noisy datasets, with a higher statistical significance than the one obtained by existing artifact removal methods. The EEGLAB-based NEAR pipeline is freely available at https://github.com/vpKumaravel/NEAR.

Calibration of KCC model for UHPC under impact and blast loadings

Ultra-high performance concrete (UHPC) is a promising material for both civil and military protective structures against the potential impact and blast loadings. However, the existing numerical simulation studies related to the material model calibrations for precisely describing the dynamic behaviors of UHPC structural members under both impact and blast loadings are limited, and the calibration procedures are also not in consensus. This paper aims to propose a comprehensive Karagozian & Case Concrete (KCC) model parameter calibration procedure and reliable parameter generation method to well predict the dynamic behavior of UHPC members subjected to dynamic loadings. First, the failure surface, equation of state (EOS), damage evolution and strain rate effect parameters were comprehensively calibrated based on the existing experimental and analytical data of UHPC, and general guidelines on the determination of key KCC model parameters for UHPC are given. Then, the numerical simulations with both the autogenerated and the calibrated parameters were carried out to predict the dynamic response and damage evolution of UHPC members, i.e., reinforced UHPC beam and column, UHPC-FST beam, and reinforced UHPC panel, under drop hammer impact and field explosion. By comparing the test data, the validation of the proposed parameter generation approach is verified. The present work can provide helpful references for designing and evaluating the UHPC structural members under potential impact and blast loadings.

Bayesian calibration of a physics-based crystal plasticity and damage model

In this work, we present a model parameter calibration procedure for a physics-based crystal plasticity model. The calibration process utilizes a powerful statistics-based Bayesian calibration method. Calibration of the crystal plasticity parameters makes use of experimentally-measured data, i.e. compressive stress–strain response, from 〈100〉 and 〈123〉 single crystal copper dynamically loaded via Kolsky bar tests. The calibration of damage parameters is achieved using experimentally-measured free-surface velocity history data from plate impact test on 〈100〉 and 〈110〉 single crystal copper, which generates shock compression followed by dynamic tensile failure. A validation assessment is then carried out by comparing the calibrated model predictions and experimental measurements of the dynamic tensile damage generated in an impacted bicrystal copper plate. Lastly, a model-informed rationale for the experimentally-observed dependence of the spatial distribution of ductile damage (porosity) on crystallography is provided.

Characterization and constitutive modeling of red clay contaminated with ammonium carbonate

AbstractConventionally, red clay is used for agricultural purposes in southern China. Fertilizers, especially urea, are commonly introduced into the red clay to improve the crop yields. However, due to the rapid urbanization in China, large areas of lands with red clays have been converted into sites for domestic and industrial constructions. Nevertheless, few researchers focused on the effect of urea on the strength and compressibility of red clay. In this research, the shear strength and compressibility of the red clay saturated by different concentrations of the (NH4)2CO3 solution (urea's hydrolysates) were experimentally investigated using direct shear as well as oedometer tests, respectively. It was noticed that both the shear strength and stiffness of the red clay significantly decreased after exposure to the urea solution. In addition, the micromechanisms of the strength and compressibility of the red clay treated by the (NH4)2CO3 were studied by the scanning electron microscope test and X‐ray diffraction test. Based on the test results, a new model was developed to simulate the chemomechanical behavior of saturated clayey soil by modifying the Barcelona basic model (BBM) for unsaturated soils. The proposed model introduces four additional parameters, compared to the BBM, to account for the nonlinear shear strength behavior of the red clay. This model accounts for most of the complex features related to the strength and stiffness behavior of clayey soils. The parameter calibration procedures, by using the oedometer and direct shear test results, are also presented. To validate the proposed model, experimental results from the literature are considered in which illite clay samples were either mechanically or chemically or both mechanically and chemically loaded. Part of the experimental results is used to calibrate the model parameters and the other part along with the calibrated parameters is used to verify the proposed model. A comparison between experimental data and predicted results demonstrated that the proposed model is able to capture the main features related to the chemomechanical coupling behavior of saturated soils.

Predicting water retention curve and resilient modulus of compacted natural and recycled pavement unbound granular materials

ABSTRACT The water retention curve (WRC) and resilient modulus (MR ) of pavement materials are essential for performing hydro-mechanical analysis on pavement structures. This paper introduces one model to predict the WRC and one model to predict the MR -moisture content relationships for compacted natural and recycled unbound granular materials (UGMs), which are typical construction materials for pavements’ base and sub-base layers. The WRC model requires UGMs’ gradation information and only one WRC measurement (preferably at optimum moisture content) for prediction. The predicted WRC can be further used in the MR model, along with measured MR at saturated and optimum moisture content conditions, to predict the influence of moisture content on the MR of UGMs. Both models require limited experimental data to enable predictions and do not need parameter calibration procedures, and thus are simple to apply. Reliability of the two models was examined using experimental data of 20 UGMs, including 10 natural and 10 recycled UGMs. It is demonstrated that both models achieved reasonable predictions for all UGMs used in this study.

Relationship between element-level and contact-level parameters of micromechanical and upscaled plasticity models for granular soils

The models for stress–strain behavior of granular soils can be generally categorized into two approaches: the conventional plasticity approach and the micromechanics-based approach. In this work, we upscale a micromechanical model to a commonly used isotropic hardening plasticity model by assuming the “same” constitutive laws at different scales. The mathematically derived relationships between element-level and contact-level parameters are presented based on the condition that the two models would predict the same mechanical response, and a parameter calibration procedure is proposed. A comparative study is then conducted to investigate the differences of the predicted element behaviors arisen from the two models.

Hydrogen informed Gurson model for hydrogen embrittlement simulation

Hydrogen-microvoid interactions were studied via unit cell analyses with different hydrogen concentrations. The absolute failure strain decreases with hydrogen concentration, but the failure loci were found to follow the same trend dependent only on stress triaxiality, in other words, the effects of geometric constraint and hydrogen on failure are decoupled. Guided by the decoupling principle, a hydrogen informed Gurson model is proposed. This model is the first practical hydrogen embrittlement simulation tool based on the hydrogen enhanced localized plasticity (HELP) mechanism. It introduces only one additional hydrogen related parameter into the Gurson model and is able to capture hydrogen enhanced internal necking failure of microvoids with accuracy; its parameter calibration procedure is straightforward and cost efficient for engineering purpose.

Biaxial hysteretic model for the characterization of quasi-static testing of shear-critical reinforced concrete columns

Given that the biaxial hysteretic model of shear-critical reinforced concrete columns is usually developed by incorporating with the single-degree-of-freedom system to write its expression in differential equation of motion control, it is unfeasible to match with the experimental data of the biaxial quasi-static testing in terms of force-displacement relationship. Therefore, a simple and effective biaxial hysteretic model is proposed in this study based on the Bouc–Wen–Baber–Noori model for characterizing the biaxial quasi-static testing of shear-critical reinforced concrete columns. An incremental numerical algorithm is then proposed to solve the pair of coupled differential equations based on the backward Euler discretization method and Newton–Raphson iteration scheme. Moreover, the influence of critical model parameters affecting the typical hysteretic characteristics of shear-critical reinforced concrete columns, such as strength deterioration, stiffness degradation, pinching phenomena, and biaxial coupling effect, is investigated, respectively, to demonstrate the capability of the biaxial hysteretic model developed. Finally, a parameter calibration procedure using the differential evolution algorithm is introduced to calibrate the magnitude of involved model parameters by comparing with the experimental data of the biaxial quasi-static testing. According to the investigation, it is found that the developed biaxial hysteretic model can reasonably describe the typical hysteretic characteristics of shear-critical reinforced concrete columns under biaxial quasi-static excitation. Following the proposed parameter calibration procedure, the biaxial hysteretic model developed produces the biaxial hysteretic loops of shear-critical reinforced concrete columns, which is in good agreement with the experimental data once the magnitude of involved model parameters is calibrated.

The Co-Terminal Swap Market Model with Bergomi Stochastic Volatility

In this article, we apply the forward variance modeling approach by L.Bergomi to the co-terminal swap market model. We build an interest rate model for which all the market price changes of hedging instruments, interest rate swaps and European swaptions, are interpreted as the state variable variations, and no diffusion parameter calibration procedure is required. The model provides quite simple profit and loss (PnL) formula, with which we can easily understand where a material PnL trend comes from when it appears, and consider how we should modify the model parameters. The model has high flexibility to control the model dynamics because parameter calibration is unnecessary and the model parameters can be used solely for the purpose of the model dynamics control. With the model, the position management of the exotic interest rate products, e.g. Bermudan swaptions, can be carried out in a more sophisticated and systematic manner. A numerical experiment is performed to show the effectiveness of the approach for a Canary swaption, which is a special form of a Bermudan swaption.

Baseflow representation module on MODCEL

ABSTRACT The land use characteristics of rural watersheds allow infiltration and consequent generation of groundwater flow, which constitutes a significant contribution to the hydrograph. Prior to this study, the MODCEL-COPPE/UFRJ model simulated only runoff, disregarding the losses occurred in rainfall-runoff process. Therefore, its application was more appropriate to urban watersheds, simulating flood events where surface flows prevail. This study aimed at representing the infiltration process and at incorporating the groundwater flow in the MODCEL’s structure, making feasible the rural watersheds simulation thus expanding its applicability as a hydrological model. A case study was performed in a 417 km2 subcatchment of Piabanha River, located at Petrópolis/RJ. It’s a predominantly rural watershed, with 80% of its area covered by forests. The model represented satisfactorily the seasonality and the magnitude of simulated recharges. In the parameter calibration procedure gave a coefficient of determination R2 = 0.75, comparing the calculated flows to the observed flows. During validation period, we obtained a coefficient of determination R2 = 0.76. The fit obtained was superior to that obtained in previous modeling of the same watershed by SMAP and MODCEL (previous version) and it was similar to TOPMODEL. In the hydrograph recession, new MODCEL presented R2 = 0.75, against 0.52 obtained in its previous version.

Investigating mechanical degradation due to fire exposure of aluminum alloy 5083 using crystal plasticity finite element method

Fire exposure causes significant mechanical property degradation of metallic materials. This paper investigates the influence of fire exposure on mechanical response of AA5083 through in situ neutron diffraction tensile tests and crystal plasticity finite element simulations. The Mechanical Threshold Stress (MTS) model is utilized, with a focus on the lattice strain response under tensile loading before and after exposure. A rigorous material parameter calibration procedure is proposed for this MTS model directly based on yield stress, plastic flow, hardening and rate dependency as a function of temperature, requiring only the macroscopic stress–strain curve. Material parameters of virgin material and fire-exposed material with full recrystallization are calibrated separately using this procedure. To initiate the investigation of mechanical degradation caused by fire exposure, a fire-exposed specimen with partial recrystallization is simulated by assigning material parameters and texture of recrystallized grains to selected grains in the virgin material according to the recrystallization fraction. Experimentally measured stress–strain curves and lattice strain evolution are employed to validate the simulations. For the first time, the local mechanical response of AA5083 is compared between in situ testing and finite element simulations, and the discrepancies of lattice strain evolution between virgin and fire-exposed material are discussed. Finally, the influence of fire-exposure on stress and lattice strain distribution is investigated to provide insight into mechanical property degradation.

Validation of a three‐dimensional constitutive model for nonlinear site response and soil‐structure interaction analyses using centrifuge test data

SummaryThe capability of a bounding surface plasticity model with a vanishing elastic region to capture the multiaxial dynamic hysteretic responses of soil deposits under broadband (eg, earthquake) excitations is explored by using data from centrifuge tests. The said model was proposed by Borja and Amies in 1994 (J. Geotech. Eng., 120, 6, 1051‐1070), which is theoretically capable of representing nonlinear soil behavior in a multiaxial setting. This is an important capability that is required for exploring and quantifying site topography, soil stratigraphy, and kinematic effects in ground motion and soil‐structure interaction analyses. Results obtained herein indicate that the model can accurately predict key response data recorded during centrifuge tests on embedded specimens—including soil pressures and bending strains for structural walls, structures' racking displacements, and surface settlements—under both low‐ and high‐amplitude seismic input motions, which was achieved after performing only a basic material parameter calibration procedure. Comparisons are also made with results obtained using equivalent linear models and a well‐known pressure‐dependent multisurface plasticity model, which suggested that the present model is generally more accurate. The numerical convergence behavior of the model in nonlinear equilibrium iterations is also explored for a variety of numerical implementation and model parameter options. To facilitate broader use by researchers and practicing engineers alike, the model is implemented as a “user material” in ABAQUS Standard for implicit time stepping.

Impact of parameter set dimensionality and calibration procedures on streamflow prediction at ungauged catchments

Spatial proximity, physical similarity and multiple linear regression are implemented on 266 snowmelt dominated catchments located in Québec, Canada. This paper evaluates: (1) the impact of the parameter set dimensionality by comparing 6, 9 and 15 free parameters structures of the GR4J hydrological model coupled to the CemaNeige snow model and; (2) the impact of the parameter set calibration method by comparing SCE-UA, CMAES and a uniform random sampling procedure. Results show that physical similarity performs better than spatial proximity and that both methods outperform multiple linear regression. Among 12 catchment descriptors, the percentage of water and geographical coordinates are the most relevant for this region. Results show that 9 free parameters are globally sufficient to regionalize the snow covered catchments but that 15 free parameters are necessary for lower quality time-series or catchments dominated by arctic or subarctic climates, high water storage capacity or low annual precipitation. Compared to complex models, parsimonious models are more robust in regionalization but their lower performance in model calibration results in lower performance in regionalization. Results show a relationship between the robustness of the parameter sets generated by the calibration procedures and their dispersion within the parameter space. Uniform random sampling is the most robust calibration method but shows an overall performance that is similar to both optimization algorithms because of its weaker performance in model calibration.

An evolving non-associated Hill48 plasticity model accounting for anisotropic hardening and r-value evolution and its application to forming limit prediction

Experimental and numerical investigations on the characterisation and prediction of cold formability of a ferritic steel sheet were performed in this study. Tensile tests and Nakajima tests were conducted for the plasticity characterisation and the forming limit diagram determination, respectively. For the plasticity behaviour description, an evolving non-associated Hill48 anisotropic plasticity model was formulated to accurately characterise the anisotropy evolution under monotonic loading, including anisotropic hardening as well as the r-value evolution. The detailed model parameter calibration procedure was also demonstrated. Eventually the model was applied to the forming limits prediction in conjunction with the modified maximum force criterion. A systematic and detailed study was performed addressing the impacts of the evolving and non-associated characteristics of the model formulation on the forming limits prediction by comparing the proposed model with the classical ones. Both the plasticity model and its application to the formability prediction were validated by the experimental results.

Fractional Derivative and Hereditary Combined Model of Flexible Polyurethane foam Viscoelastic Response under Quasi-Static Compressive Tests

The flexible polyurethane foam exhibits a highly nonlinear and viscoelastic behavior under quasi-static and uni-axial compressive tests. The dependence of the displacement rate during the loading phase is significantly higher than that of the unloading phase. A new identification of the Force-Displacement curve based on experimental observations is proposed. The total foam response is modeled as a sum of a nonlinear elastic component and a viscoelastic component. The elastic force is modeled as a sum of orthogonal polynomials in displacement, while the viscoelastic force is modeled according to the hereditary approach in the loading half-cycle and the fractional derivative approach in the unloading half-cycle. The objective of this paper is to develop a model able to make predictions of the total foam force as well as simulations of its components. A parameter calibration procedure is established according to the difference force method between two unloading responses corresponding to two different displacement rates of the same foam sample. The validity of the model results is discussed by addressing three efficiency requirements: the accuracy of simulations to experimental measurements, the repeatability of results for both tests, and the accordance of predicted components of the total response with the phenomenological identification of the Force-Displacement curve. The proposed optimization methodology is found to simulate reasonably well the responses of three different types of soft foam. Some morphologic characteristics of these foams have clear influence on viscoelastic damping and residual effects.

Systematic Calibration of Model Parameters Based on Large-Scale Experiments on Hybrid Masonry Walls

AbstractHybrid masonry is a relatively new type of structural system that benefits from the ductility and ease of construction of steel frames and from the in-plane strength and stiffness of reinforced masonry panels. Finite element analyses of hybrid masonry systems employ complex models, such as the two-scalar continuum damage model, to capture the propagation of damage through the masonry panels. Such formulations rely on several constitutive parameters but no simple experiments exist that can be used to decouple their effect and calibrate them independently. This paper proposes a method to calibrate the masonry parameters using experimental data from global system testing. Steel components are described by an elastoplastic model with kinematic hardening whose constitutive parameters are easily calibrated. A parameter calibration procedure for the damage model parameters based on the behavior of the base wall of a two-story hybrid system in global testing is proposed. In order to reduce the number of c...