Data‐driven approach for investigating and predicting of compressive strength of fly ash–slag geopolymer concrete

Abstract

AbstractFly ash–slag geopolymer concrete is an intangible material that does not use conventional Portland cement, thereby reducing CO2 emissions into the environment, and helping to increase sustainable development. However, compared with conventional concrete, the compressive strength of fly ash–slag geopolymer concrete is complexly dependent on many factors. Using the data‐driven approach for investigating and predicting fly ash–slag geopolymer concrete compressive strength is a suitable choice. This study introduces 11 easily accessible machine learning models in open‐source libraries of the Python programming language such as support vector machine, random forest (RF), gradient boosting (GB), AdaBoost, decision trees, light GB machine, extreme GB (XGB), K‐nearest neighbors, multivariable regression, Gaussian process regression, and CatBoost (CatB). Based on a dataset of 158 samples, 14 inputs, and 1 output variable compressive strength, the performance of 11 machine learning models was evaluated through 4 criteria including coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error combined with 10 repeats of 10‐fold cross‐validation. Four models have the best performance based on the above four criteria value in determining compressive strength for testing dataset sorted descending is CatB > XGB > RF > GB. Global Shapley (SHAP) value‐based CatB and XGB indicates three groups of factors with decreasing influence on compressive strength of geopolymer concrete: group I (slag, molarity, coarse aggregate, curing temperature, and alkaline activator/binder) > group II (Na2SiO3 content, NaOH content, fine aggregate, fly ash content), curing period > group III (extra water added, NaOH/Na2SiO3, superplasticizer content, rest period). Extra water added, NaOH/Na2SiO3, superplasticizer content, rest period have insignificant influence on the compressive strength value of geopolymer concrete. The greater the slag content in the slag–fly ash mixture, the greater the compressive strength of geopolymer concrete. The optimum molarity of NaOH concentration is about 14–16 M for designing the compressive strength of geopolymer concrete. SHAP values partial dependence plots (PDP) and PDP indicate that alkaline activator/binder optimal values exist to achieve high compressive strength. The compressive strength increases with curing temperature between 20 and 100°C. PDP values show that the tendency to increase compressive strength with increasing coarse aggregate content from about 750 to 1250 kg/m3.