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Global performance indicator (GPI) approach to predict the steel fiber reinforced concrete strength with error analysis

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ABSTRACT The main aim and objective of the present research is to forecast the strength of steel fiber reinforced concrete (SFRC) using four mathematical models developed with five independent variables viz. control strength, aspect ratio, percentage of steel fiber, water to cement ratio and aggregate to cement ratio. These models provide the best model with the best performance to be determined by error analysis. For obtaining the experimental data, concrete cubes of size 150 mm x 150 mm x 150 mm are tested under compression. For flexure strength, beam specimens of size 150 mm × 150 mm x 700 mm are casted with steel fiber reinforced concrete. Thus error analysis is performed for exponential mathematical models, log-linear models, analytical models and Artificial Neural Network (ANN) models to compute response variables considered in the study. The calibrated model was analyzed, compared, and ranked using the Global Performance Indicator (GPI). Difference between standard deviation of predicted and experimental data is found to be -0.082 for exponential model and for ANN model it is computed as 2.771. The exponential mathematical model shows a higher GPI value as 0.67, whereas ANN model exhibits lower GPI value as -6.94. The novelty of the work is that, this research compares the models based on the mean value, mean error, error, mean square error (MSE) and root means square error (RMSE) of the model when compared with field data. Experimental results are also provided for steel fiber reinforced concrete (SFRC). The effectiveness of GPI approach to predict the fiber reinforced concrete (FRC) strength is discussed.

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