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The accuracy of ET method in predicting the response of structures in nonlinear analysis is investigated.
Capability of ET method in predicting collapse capacity of the studied frames is discussed.
Excellent accuracy is demonstrated by the method in predicting the modal amplitudes and frequencies.
Limitations of the model reduction method in predicting secondary (derived) quantities are discussed.
Comparisons reveal the high accuracy of the proposed simple method in predicting the PD of aggregate mixtures.
However, the reliability of the method in predicting the role of the major structuring forces is less known.
This paper presents an original method in predicting the spring-back for composite aircraft structures manufactured through autoclave process.
The superiority of the SPCSV method in predicting the significant wave height over the PCSV and TPCSV methods is presented both numerically and graphically.
The accuracy of ET method in predicting the response of structures in linear and nonlinear analysis is investigated by considering a set of steel frames.
In this study, the application of artificial neural network (ANN) method in predicting the density of alkali metals and their mixtures is investigated.
The proposed RBF is proven to be the most suitable method in predicting the ranking among pairs of solutions utilized in evolutionary algorithms.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com