Prediction of strength parameters of FRP-confined concrete

Abstract This research deals with the prediction of compressive strength and crushing strain of FRP-confined concrete using neural networks and regression models. Basic information on neural networks and the types of neural networks most suitable for the analysis of experimental results are given. A set of experimental data, covering a large range of parameters, for the training and testing of neural networks is used. The prediction models based on neural network are presented. The influence of raw and the non-dimensional group of variables on compressive strength and crushing strain of FRP-confined concrete is studied through sensitivity analysis, which provided a basis for the development of a new regression based model. The neural networks based model gave high prediction accuracy and the results demonstrated that the use of neural networks in assessing the compressive strength and crushing strain of FRP-confined concrete is both practical and beneficial.

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