Compressive Strength and Slump Prediction of Two Blended Agro Waste Materials Concretes
Oluwaseye Onikeku1, *, Stanley M. Shitote2, John Mwero3, Adeola. A. Adedeji4, Christopher Kanali5
1 Civil Engineering Department, Pan African University Institute for Basic Sciences, Technology and Innovation (PAUISTI), 62000-00200 Nairobi, Kenya
2 Civil Engineering Department, Rongo University, 103-40403 Rongo, Kenya
3 Civil Engineering Department, University of Nairobi, Nairobi, Kenya
4 Civil Engineering Department, University of Ilorin, Ilorin, Nigeria
5 Agricultural Engineering Department, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Agro industrial wastes such as Bamboo Leaf Ash (BLA) and Bagasse Ash (BA) need to be employed so as to replace cement in order to produce cheaper concrete, which, in turn, save the environment.
This research focuses on the compressive strength and slump based on Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models for forecasting of compressive strength and slump value for concrete by blending BLA and BA as partial supplementary cement materials accordingly.
Three-layer perceptron was constructed through R (nnet package). A sum total of eleven artificial neural networks were formulated using 214 data sets attained from 27 laboratory concrete mixtures performed.
The neural network model forecasted the compressive strength for training, testing and validation with predicted errors of 0.802 MPa and 1.380 MPa. The model over forecasted the compressive strength averagely by 0.644 MPa and 1.905 MPa. The forecasted compressive strength changed averagely by 2.328% and 3.946%. The average difference between the predicted and experimental values was 0.588 MPa and 1.050 MPa. The coefficients of determination were 0.961 and 0.905. The MLR model predicted the slump with predictive error values of 6.634 mm and 8.374 mm. The predicted slump deviated on average by 3.633% and 8.034%. The residual error was 3.075 on 12 degrees of freedom. The multiple R2 and adjusted R2 were 0.9336 and 0.9115. The P-value was found to be 5.639e-07.
The results show that ANN and MLR are potent tools for forecasting the compressive strength and slump of concrete blending bamboo leaf ash and baggage ash. Hence, this contributes towards forecasting of the compressive strength and slump of BLA and BA blended concrete. They extends 28 days compressive strength usually found in the literature to 56 and 90 days compressive strength and there was a remarkable improvement as curing age increases. The slump of combined effect of blending BLA and BA at different percentage replacements was tested. In this study, we used BLA blended with BA to produce concrete which is an innovation.
Keywords: Compressive strength, Slump, Artificial neural network, Multiple linear regression, Bamboo leaf ash, Baggash ash, Sensitivity analysis, Model performance.
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