Experiment-driven machine learning framework for compressive strength prediction of locally sourced fiber-reinforced concrete

Abstract

This study proposes an experiment-driven machine learning (ML) framework to address experimental limitations in estimating compressive strength within a controlled experimental domain of locally sourced fiber-reinforced concrete (FRC). Concrete mixes incorporating 1, 2, and 3% lathe scrap steel fibers were prepared using a standard 1:2:4 mix with brick chips as coarse aggregate. Due to practical constraints, intermediate fiber contents (e.g., 1.5% and 2.5%) were not experimentally cast. To overcome this limitation, supervised ML models were employed to interpolate compressive strength at fractional fiber dosages, thereby reducing experimental cost and time. A total of 48 experimental observations obtained from laboratory-tested cylindrical specimens were used to develop and evaluate four ML algorithms—Gaussian Process Regression (GPR), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF)—under a five-fold cross-validation framework. Among these, GPR exhibited the best performance among the evaluated models (R² = 0.884, RMSE = 1.647 MPa), effectively capturing the nonlinear relationship between curing age, fiber content, and compressive strength. SHAP-based interpretability analysis indicated that curing age is the primary governing factor in strength development, while fiber content contributes through secondary nonlinear effects. The results further indicate that moderate fiber inclusion improves compressive strength, whereas excessive fiber content may reduce performance due to workability loss, fiber clustering, and increased matrix heterogeneity. These findings are consistent with established concrete mechanics, where fiber reinforcement plays a more dominant role in tensile behavior than in compressive strength. The proposed framework demonstrates the capability of probabilistic ML models for accurate interpolation within controlled experimental conditions using high-quality laboratory data. This study provides a practical, data-driven approach for strength estimation and mix optimization of low-cost, locally sourced FRC, supporting sustainable construction practices and efficient utilization of industrial waste materials.