Application of Adaptive Neuro-Fuzzy Inference System for Evaluating Compressive Strength of Concrete

Abstract

The objectives of this study are to develop a model for predicting the compressive strength of concrete using an adaptive neuro-fuzzy inference system (ANFIS) and validate the mix proportion using artificial neural networks (ANNs) and by experimentation. A model was developed, and the compressive strength was predicted using the ANFIS (with the subtractive clustering method of the fuzzy inference system) by MATLAB programming. In the present study, two ANFIS models were considered: ANFIS models-1 and -2. ANFIS model-1 was developed to predict the 3-day compressive strength, whereas ANFIS model-2 predicts the 28-day compressive strength by considering the 3-day compressive strength data obtained using ANFIS model-1. It was observed that the errors in the 3-and 28-day compressive strengths were 6.33%, and 17.07%, respectively. Furthermore, experiments were performed for selective mixes-M40, M50, and M60-to verify the compressive strength obtained using the ANFIS model. The model results were verified against the experimental ones based on the mixes selected from the model, and the results were found to agree with the predicted ones, with a maximum deviation of 18%. Furthermore, an ANN model was developed to predict the compressive strength to verify the accuracy of the ANFIS model. The results predicted by the ANFIS and the ANN were compared with the original results available in the literature. A significant deviation was found between the ANN model results and the original results, however, the ANN model results presented the same trend as the original results. It was concluded that the ANFIS model results were highly consistent with the original results.