Efficient machine learning algorithm with enhanced cat swarm optimization for prediction of compressive strength of GGBS-based geopolymer concrete at elevated temperature

Abstract

In order to assess building damage and develop fire safety applications, it is crucial to examine the mechanical behavior of concrete after exposure to high temperatures. Compressive strength is the most crucial mechanical characteristic of concrete which is vital for the quality assurance of engineering structures. But it is difficult to estimate the compressive strength with accuracy after geopolymer concrete (GPC) is exposed to extreme temperatures. The residual compressive strength of GGBS-based GPC is predicted in this study using a novel enhanced cat swarm-optimized extreme learning machine (ELM-ECSO) model. The ELM-ECSO compared executing statistical parameters such as the determination coefficient (R2), adjusted determination coefficient (Adj. R2), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) with simple ELM and ELM with cat swarm optimized to check its generalizability. To create a dataset for training and testing of the model, several experiments using various mix designs were carried out at high temperatures. The quasi-Monte Carlo method was implemented for the sensitivity analysis of the model. Assessment results showed that the ELM-ECSO model transcends the other two models owing to the highest prediction accuracy and showing the least error. Shapiro-Wilk statistical test was carried out to compare all the models. The sensitivity analysis indicated temperature exposure and curing age as the most impactful parameter while predicting the residual compressive strength of GPC. GPC with a Na2SiO3/NaOH ratio of 2.5 and alkaline solution/GGBS ratio of 0.40 shows better resilience against higher temperatures.