Experimental assessment with data-driven machine learning-based prediction of compressive strength of waste natural fiber-reinforced sustainable concrete.

This study presents a hybrid approach combining experimental evaluation and machine learning modeling for the compressive strength (CS) estimation of natural fiber-reinforced concrete (NFRC), which utilizes jute, coir, and bamboo fibers. A dataset of 444 concrete mix designs was compiled from the literature using seven input variables. Six advanced machine learning (ML) algorithms: Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGB), Random Forest, Gradient Boosting, Decision Tree, and K-Nearest Neighbors were trained and tested with proper hyperparameter optimization. Among these, the LGBM model demonstrated superior accuracy in prediction with an R2 of 0.8637 and the lowest root mean square error of 4.19 MPa on the testing sets. To ensure interpretability and mix design optimization, SHapley Additive Explanations (SHAP) and Partial Dependence Plot (PDP) analyses were incorporated. The dominant predictors of CS were found to be cement content, water, coarse aggregate, and supplementary cementitious materials, while the curing period and fiber content also showed a small but meaningful effect. To further validate the modeling outcomes, experimental investigations were conducted by developing 10 mix combinations with varying percentages of coir fiber, which were then subjected to compressive strength and scanning electron microscopy tests. The outcomes also validated that the incorporation of natural fiber up to 0.75% gradually increased the CS to a maximum of 41.3% after 28 days of curing, while further addition reduced performance. The dual-approach-based (ML and experimental) outcomes could assist in the sustainable advancement of the infrastructure industry as a potential solution for cost-effective, large-scale production of NFRC.