Compressive Strength Prediction of Self-Compacting Concrete Incorporating Silica Fume Using Artificial Intelligence Methods

This paper investigates the capability of utilizing Multivariate Adaptive Regression Splines (MARS) and Gene ExpressionPrograming (GEP) methods to estimate the compressive strength of self-compacting concrete (SCC) incorporating SilicaFume (SF) as a supplementary cementitious materials. In this regards, a large experimental test database was assembledfrom several published literature, and it was applied to train and test the two models proposed in this paper using thementioned artificial intelligence techniques. The data used in the proposed models are arranged in a format of seven inputparameters including water, cement, fine aggregate, specimen age, coarse aggregate, silica fume, super-plasticizer and oneoutput. To indicate the usefulness of the proposed techniques statistical criteria are checked out. The results testing datasetsare compared to experimental results and their comparisons demonstrate that the MARS (R2=0.98 and RMSE= 3.659) andGEP (R2=0.83 and RMSE= 10.362) approaches have a strong potential to predict compressive strength of SCCincorporating silica fume with great precision. Performed sensitivity analysis to assign effective parameters on compressivestrength indicates that age of specimen is the most effective variable in the mixture.

کلیدواژه ها:

Compressive Strength ، Multivariate Adaptive Regression Splines ، Gene Expression Programing ، Self Compacting Concrete ، Silica Fume