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Mohsen Khatibinia,Abdosattar Feizbakhsh,Ehsan Mohseni,Malek Mohammad Ranjbar 사단법인 한국계산역학회 2016 Computers and Concrete, An International Journal Vol.18 No.6
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.
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