Forecasting the compressive strength of GGBFS-based geopolymer concrete via ensemble predictive models

Abstract

The compressive strength (fc) of the concrete is an important parameter in the structural design. However, the assessment of fc via an experimental program is time-consuming, costly, and needs a labor force. Therefore, the forecasting of fc through different algorithms can accelerate and facilitate this process and also provide guidance for scheduling the progress of the construction. While some studies have explored the use of models for the prediction of fc of concrete, the ensemble models that can predict the fc of GPC with industrial by-products is still lacking. Within this scope, decision tree (DT), Bootstrap aggregating (Bagging), and Least-squares boosting (LSBoost) models were devised to predict fc of ground granulated blast furnace slag (GGBFS)-based geopolymer concrete (GPC). The data points collected to devise a GEP model in the previous study were used and the prediction results of the GEP model were compared with the proposed ensemble models in the current study. The age of the specimen, NaOH solution concentration, natural zeolite (NZ) content, silica fume (SF) content, and GGBFS content were used as input parameters, and fc was used as output parameter. According to ANOVA analysis, the age of the specimen was found as the most influential parameter in the determination of the fc of GGBFS-based GPC. Also, Multiple linear regression equation was proposed to estimate the fc of GGBFS-based GPC with the accuracy of 93%. The most accurate model was introduced through performance metrics and the Taylor diagram. The results proved that the highest accuracy and stable predictions were achieved by the LSBoost model with R-squared value of 98.25% followed by GEP model developed in the previous study, DT and Bagging models. However, it is worth mentioning that due to having a high coefficient of correlation values (>%80), DT and Bagging models also have an acceptable ability for predicting fc of GGBS-based GPC.