Prediction of Compressive Strength of Geopolymers Using Multi-objective Feature Selection

Abstract

To reduce the carbon dioxide emission to the environment, production of geopolymer is one of the effective binding materials to act as a substitute of cement. The strength of the geopolymer depends upon different factors such as chemical constituents, curing temperature, curing time, super plasticizer etc. In this paper, prediction models for compressive strength of geopolymer is presented using recently developed artificial intelligence techniques; multi-objective feature selection (MOFS), functional network (FN), multivariate adaptive regression spline (MARS) and multi gene genetic programming (MGGP). The MOFS is also used to find the subset of influential parameters responsible for the compressive strength of geopolymers. MOFS has been applied with artificial neural network (ANN) and non-dominated sorting genetic algorithm (NSGA II). The parameters considered for development of prediction models are curing time, NaOH concentration, Ca(OH)2 content, superplasticizer content, types of mold, types of geopolymer and H2O/Na2O molar ratio. The developed AI models were compared in terms of different statistical parameters such as average absolute error, root mean square error correlation coefficient, Nash-Sutcliff coefficient of efficiency.