Soft computing based modelling of sorptivity of concretes incorporating mineral admixtures

Joint Authors

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

Source

ZANCO Journal of Pure and Applied Sciences

Publisher

Salahaddin University-Erbil Department of Scientific Publications

Publication Date

2016-06-30

Country of Publication

Iraq

No. of Pages

6

Main Subjects

Civil Engineering

English Abstract

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.

Data Type

Conference Papers

Record ID

BIM-787475

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