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Mechanical properties of coconut shell-based concrete: experimental and optimisation modelling
Hassan Amer Algaifi1, Shahiron Shahidan2, Sharifah Salwa Mohd Zuki3
1Faculty of Civil Engineering and Built Environment, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Johor, Malaysia. hassanamer@uthm.edu.my.
Coconut shell ash (CA) effectively replaces fine aggregate in concrete, enhancing mechanical properties. Optimal replacement at 50% yields superior strength, offering a sustainable solution for waste management and construction.
Area of Science:
- Materials Science
- Civil Engineering
- Environmental Science
Background:
- Global waste accumulation necessitates sustainable material utilization in construction.
- Agricultural and industrial by-products are explored as replacements for traditional concrete aggregates.
- Coconut shell ash (CA) presents a potential eco-friendly aggregate substitute.
Purpose of the Study:
- To predict and optimize the use of coconut shell ash (CA) as a fine aggregate replacement in M30 concrete.
- To evaluate the impact of CA content on the mechanical properties of concrete.
- To develop and validate predictive models for concrete performance with CA incorporation.
Main Methods:
- Response Surface Methodology (RSM) for experimental design and optimization.
- Genetic Expression Programming (GEP) and RSM for developing predictive mathematical models.
- Analysis of Variance (ANOVA) to verify model performance and significance.
Main Results:
- Optimal CA replacement level for fine aggregate was determined to be 50%.
- Concrete with 50% CA exhibited enhanced compressive (46.2 MPa), tensile (3.74 MPa), and flexural (8.06 MPa) strength compared to control.
- GEP and RSM models demonstrated high prediction accuracy (R²=0.97 and 0.95) and low error (RMSE < 1.62 and 0.945).
Conclusions:
- Coconut shell ash (CA) is a viable and effective material for sustainable concrete production.
- Optimized CA incorporation significantly improves concrete's mechanical properties.
- GEP and RSM models provide reliable predictions for concrete performance, supporting further research and application.
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