Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)
Prediction of compressive and tensile strength of limestone via genetic programming
Expert Systems with Applications: An International Journal
Knowledge discovery of concrete material using Genetic Operation Trees
Expert Systems with Applications: An International Journal
Modelling damping ratio and shear modulus of sand-mica mixtures using genetic programming
Expert Systems with Applications: An International Journal
Neural Computing and Applications
Multi-stage genetic programming: A new strategy to nonlinear system modeling
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
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In this study, gene expression programming (GEP) is utilized to derive a new model for the prediction of compressive strength of high performance concrete (HPC) mixes. The model is developed using a comprehensive database obtained from the literature. The validity of the proposed model is verified by applying it to estimate the compressive strength of a portion of test results that are not included in the analysis. Linear and nonlinear least squares regression analyses are performed to benchmark the GEP model. Contributions of the parameters affecting the compressive strength are evaluated through a sensitivity analysis. GEP is found to be an effective method for evaluating the compressive strength of HPC mixes. The prediction performance of the optimal GEP model is better than the regression models.