# A Predictive Mathematical Model for Water Absorption of Sawdust Ash - Sand Concrete

## Authors

• Kenneth Miebaka Oba Lecturer, Department of Civil Engineering, Faculty of Engineering, Rivers State University, Port Harcourt, NIGERIA
• Ichebadu George Amadi Lecturer, Department of Civil Engineering, Faculty of Engineering, Rivers State University, Port Harcourt, NIGERIA

## Keywords:

Saw Dust Ash, Scheffe’s Simplex Lattice, Sustainability, Water Absorption of Concrete

## Abstract

Saw Dust Ash (SDA) is an industrial waste that has been used by many researchers in concrete to achieve economic and environmental sustainability. In this study, 5% of sand was replaced with SDA to produce concrete with different mix ratios. Scheffe’s simplex theory was used for five mix ratios in a {5,2} experimental design which resulted in additional ten mix ratios. Additional fifteen mix ratios were generated from the initial fifteen, for verification and testing. Concrete cubes of 150mmX150mmX150mm were formed using the thirty concrete mix ratios generated, and soaked in water for 24hours. The water absorptions of cubes from each mix ratio were determined with the standard procedure. The results of the first fifteen water absorption values were used for the calibration of the model constant coefficients, while those from the second fifteen were used for the model verification using Scheffe’s simplex lattice design. A mathematical regression model was formulated from the results, with which the water absorptions were predicted. The model was then subjected to a two-tailed t-test with 5% significance, which ascertained the model to be adequate and fit with an R2 value of 0.8244. The study also revealed that SDA can replace 5% of sand and promote environmental sustainability without significantly changing the water absorption.

2020-02-29

## How to Cite

Kenneth Miebaka Oba, & Ichebadu George Amadi. (2020). A Predictive Mathematical Model for Water Absorption of Sawdust Ash - Sand Concrete. International Journal of Engineering and Management Research, 10(1), 33–41. https://doi.org/10.31033/ijemr.10.1.7

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