A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength - Armaghani, Danial Jahed; Asteris, Panagiotis G. (Neural computing & applications, 2021)
Despite the extensive use of mortars materials in constructions over the last decades, there is not yet a reliable and robust method, available in the literature, which can estimate its strength based on its mix parameters. This limitation is due to the highly nonlinear relation between the mortar’s compressive strength and the mixed components. In this paper, the application of artificial intelligence techniques toward the prediction of the compressive strength of cement-based mortar materials with or without metakaolin has been investigated. Specifically, surrogate models (such as artificial neural network, ANN and adaptive neuro-fuzzy inference system, ANFIS models) have been developed to the prediction of the compressive strength of mortars trained using experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of both ANN and ANFIS models to approximate the compressive strength of mortars in a reliable and robust manner. Although ANFIS was able to obtain higher performance prediction to estimate the compressive strength of mortars compared to ANN model, it was found through the verification process of some other additional data, the ANFIS model has overfitted the data. Therefore, the developed ANN model has been introduced as the best predictive technique for solving problem of the compressive strength of mortars. Furthermore, using the optimum developed model an ambitious attempt to reveal the nature of mortar materials has been made.