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  • Open Access


    The Effect of Fiber Diameter on the Compressive Strength of Composites - A 3D Finite Element Based Study

    Ch,ra S. Yerramalli1, Anthony M. Waas2

    CMES-Computer Modeling in Engineering & Sciences, Vol.6, No.1, pp. 1-16, 2004, DOI:10.3970/cmes.2004.006.001

    Abstract Results from a 3D finite element based study of the compression response of unidirectional fiber reinforced polymer matrix composites (FRPC) are presented in this paper. The micromechanics based study was used to simulate the compressive response of glass and carbon fiber reinforced polymer matrix composites, with a view to understanding the effect of fiber diameter on compression strength. Results from the modeling and simulation indicate the presence of a complex three dimensional stress state in the matrix of the FRPC. Results from the simulation highlight the role of fiber diameter on the compressive response of FRPC. In particular, it is… More >

  • Open Access


    Modeling the Response of 3D Textile Composites under Compressive Loads to Predict Compressive Strength

    M. Pankow1, A.M. Waas2, C.F. Yen3

    CMC-Computers, Materials & Continua, Vol.32, No.2, pp. 81-106, 2012, DOI:10.3970/cmc.2012.032.081

    Abstract The compression response of 3D woven textile composites (3DWC) that consist of glass fiber tows and a polymer matrix material is studied using a combination of experiments and finite element based analyses. A previous study reported by the authors consisted of an experimental investigation of 3DWC under high strain rate loading, Pankow, Salvi, Waas, Yen, and Ghiorse (2011). Those experimental results were explained by using the finite element method to analyze the high rate deformation response of representative volume elements (RVEs) of the 3DWC, Pankow, Waas, Yen, and Ghiorse (2012). In this paper, the same modeling strategy is used to… More >

  • Open Access


    Prediction of Compressive Strength of Various SCC Mixes Using Relevance Vector Machine

    G. Jayaprakash1, M. P. Muthuraj2,*

    CMC-Computers, Materials & Continua, Vol.54, No.1, pp. 83-102, 2018, DOI:10.3970/cmc.2018.054.083

    Abstract This paper discusses the applicability of relevance vector machine (RVM) based regression to predict the compressive strength of various self compacting concrete (SCC) mixes. Compressive strength data various SCC mixes has been consolidated by considering the effect of water cement ratio, water binder ratio and steel fibres. Relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM has an identical functional form to the support vector machine, but provides probabilistic classification and regression. RVM is based on a Bayesian formulation of a linear model with an appropriate… More >

  • Open Access


    Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling

    Susom Dutta1, A. Ramach,ra Murthy2, Dookie Kim3, Pijush Samui4

    CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 157-174, 2017, DOI:10.3970/cmc.2017.053.167

    Abstract In the present scenario, computational modeling has gained much importance for the prediction of the properties of concrete. This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete (SCC). Three models, namely, Extreme Learning Machine (ELM), Adaptive Neuro Fuzzy Inference System (ANFIS) and Multi Adaptive Regression Spline (MARS) have been employed in the present study for the prediction of compressive strength of self compacting concrete. The contents of cement (c), sand (s), coarse aggregate (a), fly ash (f), water/powder (w/p) ratio and superplasticizer (sp) dosage have been taken as inputs… More >

  • Open Access


    Prediction of Concrete Cubic Compressive Strength Using ANN Based Size Effect Model

    Q.W. Yang1, S.G. Du1,2

    CMC-Computers, Materials & Continua, Vol.47, No.3, pp. 217-236, 2015, DOI:10.3970/cmc.2015.047.217

    Abstract Size effect is a major issue in concrete structures and occurs in concrete in any loading conditions. In this study, size effect on concrete cubic compressive strength is modeled with a back-propagation neural network. The main advantage in using an artificial neural network (ANN) technique is that the network is built directly from experimental data without any simplifying assumptions via the self-organizing capabilities of the neural network. The proposed ANN model is verified by using 27 experimental data sets collected from the literature. For the large specimens, a modified ANN is developed in the paper to further improve the forecast… More >

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