Neural Computing and Applications

, Volume 31, Supplement 2, pp 767–776 | Cite as

Application of gene expression programming to predict the compressive damage of lightweight aluminosilicate geopolymer

  • Ali NazariEmail author
Original Article


In the present study, compressive strength of lightweight aluminosilicate geopolymers produced by fine fly ash and rice husk bark ash together with palm oil clinker (POC) aggregates has been modeled by gene expression programming. To build the model, training and testing by using experimental results from 144 specimens were conducted. The used data in the models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing, and the test trial number. According to these input parameters, in the gene expression programming models, the compressive strength of each specimen was predicted. The best value of R2 and the minimum values of root mean square error (RMSE) and absolute percentage error (MAPE) are 0.9669, 2.583, and 1.984, respectively, all in training phase. The minimum value of R2 and the maximum values of RMSE and MAPE are 0.9456, 3.067, and 2.356, respectively, all in testing phase. The training and testing results in the models have shown a strong potential for predicting the compressive strength of the lightweight geopolymer specimens in the considered range and one may predict them with a tiny error.


Aluminosilicate geopolymer Compressive strength Gene expression programming 


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Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Department of Materials Science and Engineering, Saveh BranchIslamic Azad UniversitySavehIran

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