Quantifying the compressive strength of basalt fiber reinforced concrete using advanced hybrid machine learning models

Abstract

ABSTRACT Basalt Fiber Reinforced Concrete (BFRC) is being recognized as an eco-friendly advanced material with reduced environmental footprint, higher mechanical performance and long-term durability. However, its compressive strength prediction still appears to be a difficult problem due to the nonlinearity caused by the interaction of the mix components. This paper demonstrates a production-quality hybrid ML model to predict 28-day compressive strength of BFRC, providing an economical alternative to laborious and expensive laboratory-based testing. A well-defined database of 450 samples is generated involving the essential parameters, such as cement (440 kg/m3), SCMs (110 kg/m3), basalt fiber (0–4.25 kg/m3), fine aggregate (740 kg/m3), and coarse aggregate (975 kg/m3), water (121 kg/m3), superplasticizer (3.52 kg/m3), and curing period (3–365 days). The pre-processed and normalized data were partitioned into the training set (80%) and the test set (20%). Five ML models Gradient Boosting (GB), Cat Boost (CB), Light GBM (LGB), and their hybrid ensembles: GB+CB, GB+LGB were trained and compared using different metrics such as R2, RMSE, MAE, MedAE, etc. Among all ML models, the GB+LGBM model showed the best performance with R2 = 0.9445, RMSE = 5.99 MPa, MAE = 3.62 MPa, and MedAE = 2.26 MPa on the test set. SHAP analysis revealed that coarse aggregates (SHAP ≈+8) and cement (SHAP ≈+7) were the most influential factors, while the remaining water content and the excessive dosage of fiber were disadvantageous. Estimated compressive strength varied from 20 to 140 MPa. This study shows a novel approach by demonstrating the ability of ensemble ML models to capture complex concrete behavior, providing a data-driven approach for sustainable manufacturing of concrete. However, full reliance on the literature-based dataset still has significant limitations, which will increase noise, and these limitations can be further overcome by experimental validation in future studies.