Affiliations 

  • 1 Faculty of Civil Engineering and Built Environment, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Johor, Malaysia. hassanamer@uthm.edu.my
  • 2 Faculty of Civil Engineering and Built Environment, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Johor, Malaysia. shahiron@uthm.edu.my
  • 3 Faculty of Civil Engineering and Built Environment, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Johor, Malaysia
  • 4 Department of Building, School of Design and Environment, National University of Singapore, Singapore, 117566, Singapore
  • 5 Faculty of Civil Engineering Technology, Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia
PMID: 34751882 DOI: 10.1007/s11356-021-17210-1

Abstract

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 (R2 = 0.97 and 0.95, respectively). In addition, minimum prediction error (RMSE 

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.