Soft computing based modelling of sorptivity of concretes incorporating mineral admixtures

المؤلفون المشاركون

Arbili, Muhammad M.
Ghafuri, Farman K.
Mermerdas, Qasim

المصدر

ZANCO Journal of Pure and Applied Sciences

الناشر

جامعة صلاح الدين قسم النشر العلمي

تاريخ النشر

2016-06-30

دولة النشر

العراق

عدد الصفحات

6

التخصصات الرئيسية

الهندسة المدنية

الملخص الإنجليزي

In this paper, mathematical models derived from gene expression programming (GEP) and artificial neural network (NN) were used for prediction of sorptivity of concretes.

For this, 151 data samples were collected from the previous studies.

The common prediction parameters were selected as water-to-binder ratio (w/B), total binder content (B), compressive strength of 150 mm cube specimen at 28 days (fcube, 28), aggregate-to-binder ratio (Agg./B) and age of concrete (A).

Both of the proposed models were proved to be effective enough for prediction of sorptivity of concretes.

However, NN model was more accurate than GEP model.

Moreover, validation study also indicated that the proposed mathematical models can be utilized as reliable prediction tools for estimation of sorptivity of concretes.

نوع البيانات

أوراق مؤتمرات

رقم السجل

BIM-787475

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