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

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.

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► Core strength is influenced by concrete age, core size, aggregate size, L/D ratio.
► GMDH-type NN and ANFIS models were developed for predicting the concrete strength.
► GA and SVD techniques are deployed for optimal design of GMDH-type neural network.
► The results of modeling showed a high degree of coherency with the experimental results.
► The sensitivity analysis of the polynomial expression obtained from the GMDH-type NN.