Prediction of moment capacity of ferrocement composites with chicken mesh and steel slag using response surface methodology and artificial neural network

In the present study, Response Surface Methodology model (RSM) and Artificial Neural Network model (ANN) is presented to forecast the ultimate moment capacity of ferrocement using 2 variable process modelling (volume fraction and steel slag replacement). The RSM and ANN model’s outcomes are contrasted with those of other existing models, like plastic analysis, mechnaism approcah, simplified method, group method of data handling, the results shows that ferrocement with steel slag replacement of 25% and chicken mesh volume fraction (V_r) of 4.35% has maximum experimental moment capacity of 253.33 kN-mm and predicted moment capacity using RSM and ANN is 244.70 kNmm and 255.88 kNmm respectively. The adopted ANN have a regression value of 0.9882 and 0.98863 for training and testing respectively. The outcomes of the analysis of variance show that the provided models are very suitable since the p value is less than 0.005, the projected R2 and the adjustable R2 is less than 20%. Moreover, the flexural moment of ferrocement composites is more significantly affected by the V_r. According to the findings of the surface plot, Pareto chart, and regression analysis, the V_r is the most important and crucial factor for the flexural moment of ferrocement composites.

Keywords:
Steel slag; Ferrocement; Artificial Neural network; Leven Berg-Marquardt; Response Surface Methodology