Stochastic multiscale modeling of crack propagation in random heterogeneous media

Hun, Darith Anthony; Guilleminot, Johann; Yvonnet, Julien; Bornert, Michel

doi:10.1002/nme.6093

Cited by 38 publications

(11 citation statements)

References 51 publications

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“…Hun et al. 81 studied crack propagation in heterogeneous media within a probabilistic context of Monte Carlo simulations. In another work, Capillon et al.…”

Section: Methodsmentioning

confidence: 99%

Prediction of tensile strength of polymer carbon nanotube composites using practical machine learning method

2020

Journal of Composite Materials

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This paper is devoted to the development and construction of a practical Machine Learning (ML)-based model for the prediction of tensile strength of polymer carbon nanotube (CNTs) composites. To this end, a database was compiled from the available literature, composed of 11 input variables. The input variables for predicting tensile strength of nanocomposites were selected for the following main reasons: (i) type of polymer matrix, (ii) mechanical properties of polymer matrix, (iii) physical characteristics of CNTs, (iv) mechanical properties of CNTs and (v) incorporation parameters such as CNT weight fraction, CNT surface modification method and processing method. As the problem of prediction is highly dimensional (with 11 dimensions), the Gaussian Process Regression (GPR) model was selected and optimized by means of a parametric study. The correlation coefficient (R), Willmott’s index of agreement (IA), slope of regression, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were employed as error measurement criteria when training the GPR model. The GPR model exhibited good performance for both training and testing parts (RMSE = 5.982 and 5.327 MPa, MAE = 3.447 and 3.539 MPa, respectively). In addition, uncertainty analysis was also applied to estimate the prediction confidence intervals. Finally, the prediction capability of the GPR model with different ranges of values of input variables was investigated and discussed. For practical application, a Graphical User Interface (GUI) was developed in Matlab for predicting the tensile strength of nanocomposites.

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“…Hun et al. 81 studied crack propagation in heterogeneous media within a probabilistic context of Monte Carlo simulations. In another work, Capillon et al.…”

Section: Methodsmentioning

confidence: 99%

Prediction of tensile strength of polymer carbon nanotube composites using practical machine learning method

2020

Journal of Composite Materials

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“…Due to the variabilities of input variables, the response also exhibits its statistical behaviors, which are necessary to be characterized [58]. e robustness of the model and/or sensitivity of input variables could then be deduced based on statistical analysis of output response [59][60][61].…”

Section: Random Sampling Technique: Monte Carlo Methodmentioning

confidence: 99%

Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete‐Filled Steel Tubular Columns under Axial Loading

2020

Advances in Civil Engineering

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In this study, a hybrid machine learning (ML) technique was proposed to predict the bearing capacity of elliptical CFST columns under axial load. The proposed model was Adaptive Neurofuzzy Inference System (ANFIS) combined with Real Coded Genetic Algorithm (RCGA), denoted as RCGA-ANFIS. The evaluation of the model was performed using the coefficient of determination (R2) and root mean square error (RMSE). The results showed that the RCGA-ANFIS (R2 = 0.974) was more reliable and effective than conventional gradient descent (GD) technique (R2 = 0.952). The accuracy of the present work was found superior to the results published in the literature (R2 = 0.776 or 0.768) when predicting the load capacity of elliptical CFST columns. Finally, sensitivity analysis showed that the thickness of the steel tube and the minor axis length of the elliptical cross section were the most influential parameters. For practical application, a Graphical User Interface (GUI) was developed in MATLAB for researchers and engineers and to support the teaching and interpretation of the axial behavior of CFST columns.

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“…Then, the granular model can make a finer microscopic simulation in such critical zones at the grain scale. In fact, similar approaches have been applied to study the crack propagation in solids [172][173][174].…”

Section: Tablementioning

confidence: 99%

Recent advances in hot tearing during casting of aluminium alloys

Katgerman

et al. 2021

Progress in Materials Science

134

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Hot tearing is one of the most severe and irreversible casting defects for many metallic materials. In 2004, Eskin et al. published a review paper in which the development of hot tearing of aluminium alloys was evaluated . Sixteen years have passed and this domain has undergone considerable development. Nevertheless, an updated systematic description of this field has not been presented. Therefore, this article presents the latest research status of the hot tearing during the casting of aluminium alloys. The first part explains the hot tearing phenomenon and its occurrence mechanism. The second part presents a detailed description and analysis of the characterisation methods of the mushy zone mechanical properties and hot tearing susceptibility. The third part presents considerable data pertaining to the mushy zone behaviour, including those of the linear contraction and load behaviour during solidification, semi-solid strength and ductility, and characteristic points related to hot tearing. The fourth part examines the effect of the composition and casting process parameters on the hot tearing susceptibility of aluminium alloys. The fifth part describes the hot tearing simulations and the associated criteria and mechanisms. Finally, recommendations for the further development of hot tearing research are presented.

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Stochastic multiscale modeling of crack propagation in random heterogeneous media

Cited by 38 publications

References 51 publications

Prediction of tensile strength of polymer carbon nanotube composites using practical machine learning method

Prediction of tensile strength of polymer carbon nanotube composites using practical machine learning method

Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete‐Filled Steel Tubular Columns under Axial Loading

Recent advances in hot tearing during casting of aluminium alloys

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