Prediction of Ultimate Strain and Strength of FRP-Confined Concrete Cylinders Using Soft Computing Methods

Iman Mansouri, Ozgur Kisi, Pedram Sadeghian, Chang-Hwan Lee, Jong Hu
2017 Applied Sciences  
This paper investigates the effectiveness of four different soft computing methods, namely radial basis neural network (RBNN), adaptive neuro fuzzy inference system (ANFIS) with subtractive clustering (ANFIS-SC), ANFIS with fuzzy c-means clustering (ANFIS-FCM) and M5 model tree (M5Tree), for predicting the ultimate strength and strain of concrete cylinders confined with fiber-reinforced polymer (FRP) sheets. The models were compared according to the root mean square error (RMSE), mean absolute
more » ... elative error (MARE) and determination coefficient (R 2 ) criteria. Similar accuracy was obtained by RBNN and ANFIS-FCM, and they provided better estimates in modeling ultimate strength of confined concrete. The ANFIS-SC, however, performed slightly better than the RBNN and ANFIS-FCM in estimating ultimate strain of confined concrete, and M5Tree provided the worst strength and strain estimates. Finally, the effects of strain ratio and the confinement stiffness ratio on strength and strain were investigated, and the confinement stiffness ratio was shown to be more effective. wrap [13] [14] [15] . Recently, a new category of models has been proposed based on soft computing methods, such as artificial neural networks, generic algorithms, and fuzzy logic. Models in this category can handle complex databases containing a large number of independent variables, identify the sensitivity of input parameters, and provide mathematical solutions between dependent and independent variables [16] . Pham and Hadi [16] proposed the utilization of neural networks to compute the strain and compressive strength of FRP-confined columns, and the results show agreement between proposed neural network models and experimental data. Also, there are several studies related to design-oriented and analysis-oriented models [9, [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] . Lim et al. [30] proposed a new model for evaluating the ultimate condition of FRP-confined concrete using genetic programming (GP). The model was the first to establish the ultimate axial strain and hoop rupture strain expressions for FRP-confined concrete on the basis of evolutionary algorithms. The results showed that the predictions from the suggested model aligned with a database compiled by the authors. The proposed models provided improved predictions compared to the existing artificial intelligence models. The model proved that more accurate results can be achieved in explaining and formulating the ultimate condition of FRP-confined concrete. The model assessment presented in that study clearly illustrated the importance of the size of the test databases and the selected test parameters used in the development of artificial intelligence models on their overall performance. This paper studies the capability of four soft computing techniques for predicting the ultimate strength and strain of FRP-confined concrete cylindrical specimens. The computing techniques include radial basis neural network (RBNN), adaptive neuro fuzzy inference system (ANFIS) with subtractive clustering (ANFIS-SC), ANFIS with fuzzy c-means clustering (ANFIS-FCM), and M5 model tree (M5Tree). Overview of Soft Computing Approaches Radial Basis Function Neural Network Artificial neural networks (ANNs) are inspired by biological neural networks. ANNs include a set of processing components, called neurons, which operate in parallel processes and transmit information to other neurons, similar to the functioning of a biological brain. ANNs are an efficient method for modeling complex input-output relationships and can learn relationships directly from the data being modeled [31] . The nonlinearity within a radial basis function (RBF) network can be selected from a few classic nonlinear functions. The hidden layer carries out a fixed nonlinear transformation with no adjustable variables, and it maps the input onto a new layer. The output layer then performs a linear combination on this new layer, and the only adjustable variables are the weights of this linear combiner [32] . A general RBF network is schematically illustrated in Figure 1 .
doi:10.3390/app7080751 fatcat:ss2c6m4tsze2vhmoql6b4d3vou