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      Experimental and Numerical Sensitivity Assessment of Viscoelasticity for Polymer Composite Materials

      research-article
      1 , 2 ,
      Scientific Reports
      Nature Publishing Group UK
      Engineering, Materials science

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          Abstract

          Viscoelastic polymer composites are widely used for vibration control in different fields of engineering like aerospace, mechanical, and structural engineering. The viscoelastic properties of these materials are strain rate-dependent and are highly related to frequency. Yet to date, less attention has been paid to quantifying the effects of these parameters and their interactions on damping properties and providing an approximation method for further applications. In the present research, a series of experimental tests was conducted on a viscoelastic material and the experimental data were numerically analyzed in detail. Sensitivity analyses are usually applied to quantify uncertainty using sampling techniques. However, in this study a method was proposed to derive a closed-form solution using the response surface function and a derivative-based global sensitivity analysis to evaluate the output contribution of each parameter. These effects were quantified and several approximation statistics were provided for future engineering implementations. The computational evaluation conducted in this study gives a detailed insight into the mechanical behavior of viscoelastic materials.

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          The random subspace method for constructing decision forests

          Tin Ho (1998)
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            Machine learning in materials informatics: recent applications and prospects

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              Rotation forest: A new classifier ensemble method.

              We propose a method for generating classifier ensembles based on feature extraction. To create the training data for a base classifier, the feature set is randomly split into K subsets (K is a parameter of the algorithm) and Principal Component Analysis (PCA) is applied to each subset. All principal components are retained in order to preserve the variability information in the data. Thus, K axis rotations take place to form the new features for a base classifier. The idea of the rotation approach is to encourage simultaneously individual accuracy and diversity within the ensemble. Diversity is promoted through the feature extraction for each base classifier. Decision trees were chosen here because they are sensitive to rotation of the feature axes, hence the name "forest." Accuracy is sought by keeping all principal components and also using the whole data set to train each base classifier. Using WEKA, we examined the Rotation Forest ensemble on a random selection of 33 benchmark data sets from the UCI repository and compared it with Bagging, AdaBoost, and Random Forest. The results were favorable to Rotation Forest and prompted an investigation into diversity-accuracy landscape of the ensemble models. Diversity-error diagrams revealed that Rotation Forest ensembles construct individual classifiers which are more accurate than these in AdaBoost and Random Forest, and more diverse than these in Bagging, sometimes more accurate as well.
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                Author and article information

                Contributors
                jkim12@skku.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                20 January 2020
                20 January 2020
                2020
                : 10
                : 675
                Affiliations
                [1 ]ISNI 0000 0001 2181 989X, GRID grid.264381.a, Research assistant, Department of Civil & Architectural Engineering, Sungkyunkwan University, ; Suwon, Republic of Korea
                [2 ]ISNI 0000 0001 2181 989X, GRID grid.264381.a, Professor, Department of Civil & Architectural Engineering, , Sungkyunkwan University, ; Suwon, Republic of Korea
                Article
                57552
                10.1038/s41598-020-57552-3
                6971014
                31959804
                bdca9e7a-f12a-4c04-86af-8564fc3b36ca
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 August 2019
                : 6 December 2019
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                engineering,materials science
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                engineering, materials science

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