Performance comparison of neural network training algorithms in the modeling properties of steel fiber reinforced concrete

Abstract

Our study is aimed at modeling the effect of three contributory factors, namely aspect ratio, water cement ratio and cement content on the water intake/absorption, compressive strength, flexural strength, split tensile strength and slump properties of steel fiber reinforced concrete. Artificial neural network (ANN) as a multilayer perceptron normal feed forward network was integrated to develop a predictive model for the aforementioned properties. Five training algorithms belonging to three classes: gradient descent, Levenberg Marquardt (quasi Newton) and genetic algorithm (GA). The ANN configuration consists of the input layer with three nodes, a single hidden layer of ten nodes of the output layer with five nodes. The study also compared the performance of all algorithms with regards to their predicting abilities. The ANN training was done by splitting the experimental data into the training and testing set. The divergence of the RMSE between the output and target values of the test set was monitored and used as a criterion to stop training. Although the convergence speed of GA was far higher than all other training algorithm, it performed better in predicting the water intake/absorption, split tensile strength and slump properties. However, incremental back propagation (IBP) and batch back propagation (BBP) outperformed GA in predicting the compressive strength and flexural strength respectively. The overall performance of the training algorithm was assessed using the coefficient of determination and the absolute fraction of variance obtained for the test data set and GA was found to have the highest value of 0.94 and 0.92 respectively. In determining the properties fiber reinforced concrete according to GA–ANN implementation, the water/cement ratio played slightly more dominant role than the aspect ratio and this was followed by cement content.

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

“…Awolusi et al [14] stated that the comparison between ANN and some classical modeling techniques such as response surface methodology (RSM), showed the supremacy of ANN as a modeling technique in analyzing non-linear relationships of data sets, which consequently provides good fitting for data and as well as better predictive ability. Karkalos et al [15] in the comparative study between regression and neural networks for modeling Al6082-T6 alloy drilling found that the MLP_ANN models were superior to the regression model, as they were able to achieve a relatively lower prediction error.…”

“…Similar to the human brain, ANNs are capable of processing multi-dimensional, non-linear, clustered and imprecise information and could be used to extract a pattern in nonlinear, complex and noisy data sets to detect the trends with high accuracy. Thus, ANN can be used to decode complicated real world problems that are sometimes challenging to evaluate using statistical approaches without the need for complicated equations, and is capable of exploring regions that are otherwise omitted when using statistical approaches [14] [34]. They are widely used by researchers to solve a variety of problems in science and engineering, forecasting, multivariate data analysis using experimental data, field observations or even incomplete or fuzzy data sets particularly for some areas where the conventional modeling methods fail such as prediction of internal combustion engine performance characteristics [35].…”

Performance comparison of feedforward neural network training algorithms in modelling microwave pyrolysis of oil palm fibre for hydrogen and biochar production

“…Pyrolysis is a popular option for thermal decomposition of biomass in the absence of oxygen. 18,19 There is no multiple training algorithms performance comparison study published for microwave pyrolysis process. 8 Biochar is a carbon rich, solid product of biomass pyrolysis.…”

“…For example, three training algorithms were compared for wind speed forecasting and performance of five training algorithms that were compared in modelling of steel fibre reinforced concrete. 18,19 There is no multiple training algorithms performance comparison study published for microwave pyrolysis process. This paper will compare the performance of 11 different training algorithms.…”

<strong></strong>Comparison of Response Surface Methodology and Hybrid-Training Approach of Artificial Neural Network in Modeling the Properties of Concrete Containing Steel Fiber Extracted from Waste Tires

“…Comparison of Experimental values (red) with predicted values (blue). The straight line represents the linear regression[33].…”