Metamodel techniques to estimate the compressive strength of UHPFRC using various mix proportions and a high range of curing temperatures

In order to predict the compressive strength (σc) of Ultra-high performance fiber reinforced concrete (UHPFRC), developing a reliable and precise technique based on all main concrete components is a cost-effective and time-consuming process. To predict the UHPFRC compressive strength, four different soft computing techniques were developed, including the nonlinear- relationship (NLR), pure quadratic, M5P-tree (M5P), and artificial neural network (ANN) models. Thus, 274 data were collected from previous studies and analyzed to evaluate the effect of 11 variables that impact the compressive strength, including curing temperature. The performance of the predicted models was evaluated using several statistical assessment tools. According to the findings, ANN results performed more suitable than other models with the lowest root mean square error (RMSE) and highest coefficient of determination (R2) value. According to the sensitivity analysis, the most variables that affect the compressive strength prediction of UHPFRC are a curing temperature with a percentage of 17.36%, the fiber content of 17.13%, and curing time of 15.13%