Artificial Intelligence and Expert Systems for Engineers
Artificial Intelligence and Expert Systems for Engineers
Fuzzy polynomial neural networks for approximation of the compressive strength of concrete
Applied Soft Computing
Adaptive neuro-fuzzy computing technique for suspended sediment estimation
Advances in Engineering Software
Advances in Engineering Software
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 12.05 |
In this paper, Adaptive Neural Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) models are discussed to determine peak pressure load measurements of the 0, 0.2, 0.4 and 0.6% glass fibers (by weight) reinforced concrete pipes having 200, 300, 400, 500 and 600mm diameters. For comparing the ANFIS, MLR and experimental results, determination coefficient (R^2), root mean square error (RMSE) and standard error of estimates (SEE) statistics were used as evaluation criteria. It is concluded that ANFIS and MLR are practical methods for predicting the peak pressure load (PPL) values of the concrete pipes containing glass fibers and PPL values can be predicted using ANFIS and MLR without attempting any experiments in a quite short period of time with tiny error rates. Furthermore ANFIS model has the predicting potential better than MLR.