Predicting of compressive strength of recycled aggregate concrete by genetic programming

Gholamreza Abdollahzadeh , Ehsan Jahani , Zahra Kashir

Abstract

This paper, proposes 20 models for predicting compressive strength of recycled aggregate concrete (RAC) containing silica fume by using gene expression programming (GEP). To construct the models, experimental data of 228 specimens produced from 61 different mixtures were collected from the literature. 80% of data sets were used in the training phase and the remained 20% in testing phase. Input variables were arranged in a format of seven input parameters including age of the specimen, cement content, water content, natural aggregates content, recycled aggregates content, silica fume content and amount of superplasticizer. The training and testing showed the models have good conformity with experimental results for predicting the compressive strength of recycled aggregate concrete containing silica fume.

Key Words

recycled aggregate concrete; silica fume; compressive strength; gene expression programming

Address

Gholamreza Abdollahzadeh — Department of Civil Engineering, Babol University of Technology, Babol, Iran
Ehsan Jahani — Department of Civil Engineering, University of Mazandaran, Babolsar, Iran
Zahra Kashir — Department of Technology, Tabari University of Babol, Babol, Iran

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