PERFORMANCE OF ARTIFICIAL NEURAL NETWORKS AND FUZZY INFERENCE SYSTEMS FOR PREDICTING CONCRETE COMPRESSIVE STRENGTH

Resumen

Artificial neural networks (ANN) have been traditionally utilized for developing prediction models based on experimental data; however, fuzzy logic theory is a novel tool that could also be applied as well in this cases. The present study compares the performance of two different approaches; namely, ANN and fuzzy inference systems (FIS) for predicting concrete compressive strength. Adaptive neuro fuzzy inference systems (ANFIS) was the technique based on input – output experimental data that was used to create two Sugeno type fuzzy models for estimating concrete compressive strength and then compared with several ANN prediction models created through different methods. Subtractive clustering method was the clustering procedure for establishing the number of membership functions and fuzzy rules. Predicted data resulting from all models were presented in a comparative manner and validation analyses were conducted in order to observe the performance of ANN and ANFIS. The results indicate that both fuzzy models performed very well when estimating concrete compressive strength (i.e., all R2 values are greater than 90%) while for ANN, only one ANN method had an R2 value greater than 90%.