Multivariate Dataset and Response Surface Optimization for Predicting Mechanical and Durability Properties of Slurry-Infiltrated Fibrous Concrete Incorporating Sisal Fiber and Coffee Husk Ash

Mechanical performance particularly compressive, tensile, and flexural strength is fundamental to structural concrete design, governing load-bearing capacity, failure mechanisms, and overall infrastructure resilience. Conventional concrete, while widely used, suffers from low tensile strength (8–15% of compressive strength) and brittle failure, limitations that have driven the development of Slurry-Infiltrated Fibrous Concrete (SIFCON), which achieves exceptional mechanical properties (compressive strength >100 MPa, tensile strength 10–25 MPa, flexural strength 30–50 MPa) through a high-volume fiber network (5–12%) infiltrated with a low water-to-binder slurry. However, traditional SIFCON relies on costly synthetic fibers and high cement content (>800 kg/m³), raising economic and environmental concerns. Coffee husk ash (CHA), an agricultural waste with high amorphous silica content (60–70%), serves as a pozzolanic supplementary cementitious material that refines pore structure, consumes calcium hydroxide, strengthens the interfacial transition zone (ITZ), and reduces pore solution alkalinity—thereby protecting natural fibers from alkaline degradation. Sisal fiber, a renewable, low-cost natural fiber with tensile strength of 400–700 MPa, offers ductility and crack-bridging capacity but suffers from age-related embrittlement in alkaline cementitious environments. The combined use of CHA and sisal fiber in SIFCON creates a synergistic ternary composite where CHA protects sisal fibers from degradation while densifying the matrix and ITZ, and the high fiber volume fraction compensates for any early-age strength reduction. Despite this potential, no systematic study has optimized the ternary SIFCON system containing CHA and sisal fiber. Therefore, this study employs Response Surface Methodology (RSM) with a multivariate dataset to model nonlinear interactions, perform multi-objective optimization, and develop a sustainable, high-performance SIFCON using agricultural wastes for structural applications