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 expression programing (gep) methods to estimate the compressive strength of self-compacting concrete (scc) incorporating silica fume (sf) as a supplementary cementitious materials. in this regards, a large experimental test database was assembled from several published literature, and it was applied to train and test the two models proposed in this paper using the mentioned artificial intelligence techniques. the data used in the proposed models are arranged in a format of seven input parameters including water, cement, fine aggregate, specimen age, coarse aggregate, silica fume, super-plasticizer and one output. to indicate the usefulness of the proposed techniques statistical criteria are checked out. the results testing datasets are compared to experimental results and their comparisons demonstrate that the mars (r²=0.98 and rmse= 3.659) and gep (r²=0.83 and rmse= 10.362) approaches have a strong potential to predict compressive strength of scc incorporating silica fume with great precision. performed sensitivity analysis to assign effective parameters on compressive strength indicates that age of specimen is the most effective variable in the mixture.