Presenting a model for estimating the cube compressive strength of self-compacting concrete in cast in-situ piles using GEP.

The cast in-situ pile is a widely used type of deep foundations which its execution in civil projects is increasing daily. The use of ordinary concrete in this type of piles causes technical and executive problems, a decrease in the compressive strength (CS) of concrete, and an increase in the permeability under the ground level. But use of the self-compacting concrete in the cast in-situ piles while increasing the CS of concrete ensures proper compaction, increase in the execution speed, and easy placing of concrete. In this article, utilizing the data obtained from the laboratory results and also the application of soft computing techniques, predicting the degree of CS of self-compacting concrete (SCC) in concrete piles was investigated. To estimate the CS of SCC, a total number of 7 inputs were implemented. Then, using gene expression programming (GEP) a model was presented for estimating the CS of SCC in the cast in-situ piles. The results of the neural network showed a precision of 99.98% which exhibits the high accuracy of the model. The use of this model could greatly help persons, companies, and research centers in the preparation and construction of self-compacting concrete with the desired CS.