Adeniji, A., Kupolati, W., Burger, E., Are, M., Snyman, J., Ndambuki, J., Ackerman, C. (2025). An artificial neural network model prediction of mortar strengths reinforced with henequen and sisal fibres for sustainable construction. , 43(11), 891-904. doi: 10.30684/etj.2025.161598.1971
Akintayo A. Adeniji; Williams K. Kupolati; Everardt Burger; Mueez Are; Jacques Snyman; Julius Ndambuki; Chris Ackerman. "An artificial neural network model prediction of mortar strengths reinforced with henequen and sisal fibres for sustainable construction". , 43, 11, 2025, 891-904. doi: 10.30684/etj.2025.161598.1971
Adeniji, A., Kupolati, W., Burger, E., Are, M., Snyman, J., Ndambuki, J., Ackerman, C. (2025). 'An artificial neural network model prediction of mortar strengths reinforced with henequen and sisal fibres for sustainable construction', , 43(11), pp. 891-904. doi: 10.30684/etj.2025.161598.1971
Adeniji, A., Kupolati, W., Burger, E., Are, M., Snyman, J., Ndambuki, J., Ackerman, C. An artificial neural network model prediction of mortar strengths reinforced with henequen and sisal fibres for sustainable construction. , 2025; 43(11): 891-904. doi: 10.30684/etj.2025.161598.1971

An artificial neural network model prediction of mortar strengths reinforced with henequen and sisal fibres for sustainable construction

Engineering and Technology Journal
Volume 43, Issue 11, November 2025, Pages 891-904    PDF (1.18 M)
Document Type: Research Paper
DOI: 10.30684/etj.2025.161598.1971
Authors
Akintayo A. Adeniji* 1; Williams K. Kupolati2; Everardt Burger3; Mueez Are4; Jacques Snyman2; Julius Ndambuki2; Chris Ackerman2
1Civil Engineering Dept., Tshwane University of Technology, Pretoria, South Africa. Civil Engineering Dept., University of Ibadan, Ibadan, Nigeria.
2Civil Engineering Dept., Tshwane University of Technology, Pretoria, South Africa.
3a Civil Engineering Dept., Tshwane University of Technology, Pretoria, South Africa.
4Civil Engineering Dept., University of Ibadan, Ibadan, Nigeria.
Abstract
The increasing demand for sustainable building materials has spurred interest in using natural fibers for construction. This study evaluates the performance of henequen and sisal fibers in mortar to enhance their tensile properties and provide alternatives to synthetic fibers. Mortar samples were prepared using a 1:3 mix ratio, with fibers introduced at 0%, 0.5%, 1%, 1.5%, and 2% by weight. The particle size distribution, setting times, consistency, and water ab-sorption and tensile strength of mortar constituents and fibers were determined. Compressive and splitting tensile strength tests were conducted on samples of 75 mm cubes and 50 × 100 mm cylinders at 7, 14, 21, and 28 days, respectively. Data were analyzed using ANOVA at α = 0.05 and a validated Artificial Neural Network (ANN) model. Henequen fiber exhibited lower water absorption and higher tensile strength. The 1% mixture recorded the highest com-pressive strength of 15.08 MPa and 15.41 MPa, and the split tensile strength at 0.5% mixture was 3.43 MPa and 3.51 MPa at 28 days, respectively. ANOVA confirmed significant dif-ferences in tensile strength due to fiber inclusion, and compressive strength results indicated no statistically significant differences across samples. The ANN demonstrated high predic-tive accuracy, with R² values of 0.8785 and 0.7885, and low mean squared errors of 0.3855 and 0.4855, for compressive and split tensile strengths, supporting its use in performance forecasting. The optimum compressive strength was observed at a 1% fiber content, while the split tensile strength improved up to 0.5%. The experimental results support its application in sustainable construction.

Highlights
  • Henequen fibers were extracted from Agave fourcroydes, and sisal fibers were locally sourced
  • Mortar was prepared using a 1:3 cement-to-sand ratio with fibers added in various weight percentages
  • Test samples were cast in cube and prism molds and evaluated for compressive and tensile strength
  • Fiber content had a strong influence on the mechanical performance of the mortar
  • An artificial neural network model was developed to predict the strength properties of the mixes
Keywords
Henequen fiber; Sisal fiber; Reinforced mortar; Sustainable construction; Artificial neural network
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