Prediction of the concrete compressive strength by means of core testing using GMDH-type neural network and ANFIS models

Abstract Assessment of insitu concrete strength by means of cores cut from hardened concrete is accepted as the most common method, but may be affected by many factors. Group method of data handling (GMDH) type neural networks and adaptive neuro-fuzzy inference systems (ANFIS) were developed based on results obtained experimentally in this work along with published data by other researchers. Genetic algorithm (GA) and singular value decomposition (SVD) techniques are deployed for optimal design of GMDH-type neural networks. Samples incorporated six parameters with core strength, length-to-diameter ratio, core diameter, aggregate size and concrete age considered as inputs and standard cube strength regarded as the output. The results show that a generalized GMDH-type neural network and ANFIS have great ability as a feasible tool for prediction of the concrete compressive strength on the basis of core testing. Moreover, sensitivity analysis has been carried out on the model obtained by GMDH-type neural network to study the influence of input parameters on model output.