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Computers and Concrete
  Volume 18, Number 6, December 2016, pages 1065-1082

Modeling mechanical strength of self–compacting mortar containing nanoparticles using wavelet–based support vector machine
Mohsen Khatibinia, Abdosattar Feizbakhsh, Ehsan Mohseni and Malek Mohammad Ranjbar

    The main aim of this study is to predict the compressive and flexural strengths of self–compacting mortar (SCM) containing nano–SiO2, nano–Fe2O3 and nano–CuO using wavelet–based weighted least squares–support vector machines (WLS–SVM) approach which is called WWLS–SVM. The WWLS–SVM regression model is a relatively new metamodel has been successfully introduced as an excellent machine learning algorithm to engineering problems and has yielded encouraging results. In order to achieve the aim of this study, first, the WLS–SVM and WWLS–SVM models are developed based on a database. In the database, nine variables which consist of cement, sand, NS, NF, NC, superplasticizer dosage, slump flow diameter and V–funnel flow time are considered as the input parameters of the models. The compressive and flexural strengths of SCM are also chosen as the output parameters of the models. Finally, a statistical analysis is performed to demonstrate the generality performance of the models for predicting the compressive and flexural strengths. The numerical results show that both of these metamodels have good performance in the desirable accuracy and applicability. Furthermore, by adopting these predicting metamodels, the considerable cost and time–consuming laboratory tests can be eliminated.
Key Words
    model mortar; compressive strength; flexural strength; nanoparticles; weighted least squares support vector machine; wavelet
Mohsen Khatibinia and Abdosattar Feizbakhsh: Department of Civil Engineering, University of Birjand, Birjand, Iran

Ehsan Mohseni and Malek Mohammad Ranjbar: Department of Civil Engineering, University of Guilan, Rasht, Iran

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