DEVELOPMENT OF A HIGH-PERFORMANCE FIBER-REINFORCED MORTAR COMPOSITE AS A SUSTAINABLE BUILDING MATERIAL

Abstract

The construction sector is responsible for 5-10% of the global CO₂ emissions, most of which come from the production of concrete. The research interest is shifting towards green cementitious and fiber composites as an alternative to traditional materials, as they are lightweight, renewable and efficient. Traditional decorative and insulation panels in tropical countries, like Nigeria, are usually made with harmful chemicals, and coconut coir, an abundant agricultural by-product, is not widely used. This study aims to create a coir–glass fiber reinforced mortar composite to improve the indoor environmental quality and durability of buildings. It also includes supplementary cementitious materials, Palm Kernel Shell Ash (PKSA) and Ground Granulated Blast Slag (GGBS) and looks at gaps in literature regarding fiber lengths <10 mm and mid-range fiber content (0.1–0.6%). The coir fibers are treated with alkali solution for improving performance. The physical and mechanical properties, water absorption, thermal conductivity and the microstructural behavior (SEM-EDX, XRF) of the composite were evaluated. The neural fitting tool provided in MATLAB®2025a was used to model and predict the compressive strength. The results indicated that mercerization process led to decrease in fiber diameters (10-30%) and increased the resistance to chemicals. The results of mechanical analysis showed that low coir content and high glass content (PC+CF0.1+GF0.6) had the highest compressive and flexural strength, whereas high coir content had led to an increase in air voids and decreased matrix compactness. Additionally, the combination of 20% GGBS and PKSA in PC+CF0.2+GF0.5 sample has the best performance in both mortar and concrete. Also, samples containing PKSA exhibited the lowest thermal conductivity (K ≈ 0.0875 W/mK), establishing its potential as an effective insulation material for tropical microclimate. Good correlation between experimental and predicted strength (up to R2 = 0.976 and Pearson's r = 0.988) was obtained and the sensitivity analysis was used to analyze the influence of input parameters on the ANN prediction. The research contributes to the achievement of Sustainable Development Goals (SDG 3, 9, 11, 12) as it aims to convert agricultural waste into valuable building materials for green buildings in the humid tropics.