Fuzzy polynomial neural networks for approximation of the compressive strength of concrete

The main purpose of this paper is to develop fuzzy polynomial neural networks (FPNN) to predict the compressive strength of concrete. Two different architectures of FPNN are addressed (Type1 and Type2) and their training methods are discussed. In this research, the proposed FPNN is a combination of fuzzy neural networks (FNNs) and polynomial neural networks (PNNs). Here, while the FNN demonstrates the premises (If-Part) of the fuzzy model, the PNN is implemented as its consequence (Then-Part). To enhance the performance of the network, back propagation (BP), and list square error (LSE) algorithms are utilized for the tuning of the system.Six different FPNN architectures are constructed, trained, and tested using the experimental data of 458 different concrete mix-designs collected from three distinct sources. The data are organized in a format of six input parameters of concrete ingredients and one output as 28-day compressive strength of the mix-design. Using root means square (RMS) and correlation factors (CFs), the models are evaluated and compared with training and testing data pairs. The results show that FPNN-Type1 has strong potential as a feasible tool for prediction of the compressive strength of concrete mix-design. However, the FPNN-Type2 is recognized as unfeasible model to this purpose.