Journal
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
Volume 29, Issue 14, Pages 21140-21155Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11356-021-17210-1
Keywords
Environmental-friendly concrete; Waste material; Coconut shell; Optimisation and prediction model
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Funding
- Ministry of Higher Education (MOHE) through Fundamental Research Grant Scheme [FRGS/1/2018/TK01/UTHM/02/3]
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The study evaluated and optimized the use of coconut shell as a replacement for fine aggregate in concrete. Experimental tests and modeling led to the determination of the optimum content of 50% replacement, with improved mechanical properties of the concrete observed.
Excessive accumulation of waste materials has presented a serious environmental problem on a global scale. This has prompted many researchers to utilise agricultural, industrial, and by-product waste materials as the replacement of aggregate in the concrete matrix. In this present study, the prediction and optimisation of coconut shell (CA) content as the replacement of fine aggregate were evaluated based on the mechanical properties of the concrete (M30). Based on the suggested design array from the response surface methodology (RSM) model, experimental tests were carried out to achieve the goal of this study. The collected data was used to develop mathematical predictive equations using both GEP and RSM models. Analysis of variance (ANOVA) was also taken into account to appraise and verify the performance of the proposed models. Based on the results, the optimum content of replacing CA was 50%. In particular, the compressive, tensile, and flexural strength obtained after 28 days of curing were 46.2, 3.74, and 8.06 MPa, respectively, from the RSM model and 46.18, 3.85, and 7.99 MPa, respectively, from the GEP model. The obtained values were superior to those of the control concrete sample (43.12, 3.51 and 7.14 MPa, respectively). Beyond the optimum content, a loss in strength was observed. It was also found that both the GEP and RSM models exhibited high prediction accuracy with strong correlations (R-2 =0.97 and 0.95, respectively). In addition, minimum prediction error (RMSE< 0.945 (RSM), RMSE < 1.62 (GEP)) was achieved, indicating that both models were robust and reliable for further prediction. It was concluded that CA could serve as an excellent strategic material to address several serious environmental issues.
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