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Has files: falseSubject: Regression analysis

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    Article
    Citation - WoS: 11
    Citation - Scopus: 12
    Predictive Models for Mechanical Properties of Expanded Polystyrene (eps) Geofoam Using Regression Analysis and Artificial Neural Networks
    (Springer London Ltd, 2022) Akis, E.; Guven, G.; Lotfisadigh, B.
    Initial elastic modulus and compressive strength are the two most important engineering properties for modeling and design of EPS geofoams, which are extensively used in civil engineering applications such as light-fill material embankments, retaining structures, and slope stabilization. Estimating these properties based on geometric and physical parameters is of great importance. In this study, the compressive strength and modulus of elasticity values are obtained by performing 356 unconfined compression tests on EPS geofoam samples with different shapes (cubic or disc), dimensions, loading rates, and density values. Using these test results, the mechanical properties of the specimens are predicted by linear regression and artificial neural network (ANN) methods. Both methods predicted the initial modulus of elasticity (E-i), 1% strain (sigma(1)), 5% strain (sigma(5)), and 10% strain (sigma(10)) strength values on a satisfactory level with a coefficient of correlation (R-2) values of greater than 0.901. The only exception was in prediction of sigma(1) and E-i in disc-shaped samples by linear regression method where the R-2 value was around 0.558. The results obtained from linear regression and ANN approaches show that ANN slightly outperform linear regression prediction for E-i and sigma(1) properties. The outcomes of the two methods are also compared with results of relevant studies, and it is observed that the calculated values are consistent with the results from the literature.
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    Article
    Derivation of Empirical Equations for Neutronic Performance in a Thorium Fusion Breeder With Various Coolants Using Regression Analysis
    (Pergamon-elsevier Science Ltd, 2011) Acir, Adem; Alakoc, Nilufer Pekin
    In this paper, regression analyses (RA) are presented for the neutronic calculation of ThO2 mixed (CmO2)-Cm-244 fuel with different neutronic parameters for various coolants, natural lithium, Li20Sn80 and Flinabe, respectively. The tritium breeding ratio (TBR), energy multiplication factor (M), total fission rate (Xi) and Th-232(n, gamma) reaction is computed by XSDRNPM. In addition, this numerical results are estimated by RA depends on neutronic parameters and the empirical equations for neutronic performance are acquired. The results obtained by using XSDRNPM and the results of the RA, obtained empirical equations, are compared. The empirical equations indicate that RA can successfully be used for the prediction of the neutronic performance parameters in the hybrid reactor with a high degree of accuracy. In addition, correlation matrix is calculated to determined statistical relationships between variables TBR, M, Sigma(f), and Th-232(n, gamma). (C) 2011 Elsevier Ltd. All rights reserved.