A data-driven study for evaluating the compressive strength of high-strength concrete

摘要

To estimate the compressive strength of high-strength concrete (HSC), a hybrid model integrating the firefly algorithm (FFA) and fuzzy c-means (FCM) clustering method into the adaptive neuro fuzzy inference system (ANFIS) was developed in this paper. The FFA and FCM techniques were utilized to improve the forecasting accuracy of the proposed ANFIS. To establish the hybrid ANFIS-FFA model, five main constituents of HSC, cement, water, fine and coarse aggregates, and superplasticizer, are considered the input variables, and the compressive strength of HSC is used as the output variable. A comparison was conducted among four artificial intelligence models, including the proposed ANFIS-FFA model, the traditional ANFIS, the back propagation neural network (BPNN) and the extreme learning machine (ELM), in terms of four statistical indices. In addition, a detailed parametric study was conducted to investigate the influence of each input variable on the compressive strength of HSC. The results showed that the developed ANFIS-FFA model exhibits greater accuracy than the other three models, with a higher correlation coefficient (R) and lower root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) values, and it has great potential to accurately estimate the compressive strength of HSC.