Amir Ali Shahmansouri - Department of Civil Engineering, University of Mazandaran, Babolsar, Iran
Habib Akbarzadeh Bengar - Department of Civil Engineering, University of Mazandaran, Babolsar, Iran

Gene expression programming (GEP) has been broadly applied to predict the various properties of concrete. For predicting electrical resistivity of concrete containing clinoptilolite, various models were proposed by using GEP. To construct the models, experimental data were obtained through manufacturing in the laboratory. From the total dataset, 80% were utilized in the training stage and the continued 20% in the testing stage. Eight input parameters comprising the age of the specimen, cement content, water content, gravel content (separated to G20 and G10), sand content, clinoptilolite content and amount of superplasticizer were settled as input variables. The consequences also demonstrated the great potential of suggested GEP models in predicting the electrical resistivity of concrete containing clinoptilolite.

Gene expression programming, Concrete, Clinoptilolite, Electrical resistivity, Prediction