Prediction of the Mechanical Properties of Fibre-Reinforced Quarry Dust Concrete Using Response Surface and Artificial Neural Network Techniques

Abstract

The focus of this study is to forecast the 28-day compressive strength and split tensile strength of concrete with various percentages of jute and coconut fibres mixed with quarry dust. The response surface methodology (RSM) and the artificial neural networks (ANN) methods were adopted for 3 variable process modelling (coconut fibres of 0% to 2.5%, jute fibres of 0% to 2.5%, and quarry dust of 0% to 25% by weight of cement). The RSM Box−Behnken design (BBD) method was adopted to design the experiments. Test results showed that compressive strength of 34.6 N/mm2 was obtained for concrete with 0% jute, 0% coir, and 12.5% quarry dust. Similarly, the maximum split tensile strength of 3.8 N/mm2 was obtained for concrete with 1.25% jute fibres, 1.25% coconut fibres, and 12.5% quarry dust. ANOVA and Pareto charts were used to assess regression models for response data. Each progression variable’s statistical significance was assessed, and the resulting models were expressed as second-order polynomial equations. Levenberg−Marquardt (LM) algorithm with feed-forward back propagation neural network was used for assessing the compressive strength and split tensile strength of concrete. The statistical data, root mean square error (RMSE), mean absolute error (MAE), mean absolute and percentage error (MAPE), and determination coefficient (R2) show that both techniques, ANN and RSM, are effective tools for predicting compressive strength and split tensile strength. Furthermore, RSM and ANN models have a high correlation with experimental data. However, the response surface methodology model is more accurate.

Prediction of strength properties of concrete with jute fibres, kenaf fibers and silica fumes: a response surface methodology (DOE) approach

“…The outputs of RSM and ANN models using real data show that the models can make precise predictions of concrete qualities. RSM models surpass ANN prediction, based on results from contrasting the two methods, with a determination coincident of almost 1 [21]. The research findings indicate that there have been relatively few studies exploring the collective impact of KF, JF and SF on the mechanical properties of concrete.…”

“…The computed response includes compressive strength fck 14 , fck 28 and split tensile strength fts 14 fts 28 . Equation 1 [21] shows how to express the obtained response.…”

“…A second-order model, represented by Equation 2 [21], was assumed and utilized to elucidate the relationship between the combination of variables and response function. This model helps explain the variations in the strength properties of concrete.…”

“…Jute and kenaf fibre may provide a bridging effect or act as an anchor, increasing the tensile strength of the material. The existence of dispersed fibers in the concrete facilitated a bridge effect between the fibers and the concrete matrix, leading to an enhancement in the tensile strength of the hardened material [2,21].…”

Research on Layered Steel Fiber Reinforced Concrete Mix Ratio Design Based on Orthogonal Test

“…Response surface analysis can effectively analyze the impact of various factors on the dependent variable and determine the optimization direction and range of each factor [22][23][24][25][26][27]. Based on the range analysis of the experimental results shown in Table 12, the three factors that had the greatest impact on the mechanical properties of concrete were selected for response surface analysis.…”