Compressive strength prediction of coconut fiber reinforced concrete using PSO optimized explainable machine learning.

The use of natural fibers in concrete is a sustainable strategy to improve ductility and crack resistance; however, coconut fiber can introduce nonlinear interactions among mix constituents that complicate compressive strength prediction. This study proposes an optimized and explainable machine learning framework for compressive strength prediction of coconut fiber reinforced concrete (CFRC). Six regression algorithms, support vector machine (SVM), k-nearest neighbors (KNN), random forest (RF), light gradient boosting (LGB), extreme gradient boosting (XGB), and artificial neural network (ANN) were optimized using particle swarm optimization (PSO) on 586 experimental samples with eight inputs: cement, fine aggregate (FA), natural coarse aggregate (NCA), recycled coarse aggregate (RCA), water, fiber content, fiber length, and age. A nested grouped cross-validation scheme (outer 5-fold, inner 3-fold) was adopted for robust model selection, followed by evaluation on a locked 30% test set. The best model, XGB-PSO, achieved mean cross-validation performance of R