Recently, addition of various natural fibers to high strength concrete has aroused great interest in the field of building materials. This is because natural fibers are much cheaper and locally available, as compare to synthetic fibers. Keeping in view, this current research conducted mainly focuses on the static properties of hybridized (sisal/coir), sisal and coir fiber-reinforced concrete. Two types of natural fibers sisal and coir were used in the experiment with different lengths of 10, 20 and 30 mm and various natural fiber concentrations of 0.5%, 1.0%, and 1.5% by mass of cement, to investigate the static properties of sisal fiber reinforced concrete (SFRC), coir fiber reinforced concrete (CFRC) and hybrid fiber reinforced concrete (HFRC). The results indicate that HFRC has increased the compressive strength up to 35.98% with the length of 20 mm and with 0.5% concentration, while the CFRC and SFRC with the length of 10 mm and with 1% concentration have increased the compressive strength up to 33.94% and 24.86%, respectively. On another hand, the split tensile strength was increased by HFRC with the length of 20 mm and with 1% concentration, CFRC with the length of 10 mm and with 1.5% concentration, and SFRC with the length of 30 mm and with 1% concentration have increased up to 25.48%, 24.56% and 11.80%, respectively, while the HFRC with the length of 20 mm and with 0.5% concentration has increased the compressive strength of concrete but has decreased the split tensile strength up to 2.28% compared to PC. Overall, using the HFRC with the length of 20 mm and with 1% concentration provide the maximum output in terms of split tensile strength.

This work aims to compare the compressive strength of cellulose nanofibrils (CNFs) reinforced concrete, cement paste, and cement mortar utilizing machine learning models for prediction before construction. To obtain this goal, the ten supervised regression ML models were executed. The datasets with an experimental foundation consisting of 266, 233, and 196 data points for cement paste, cement mortar, and concrete respectively were set and split into training and testing groups for the models' execution. There were seven input parameters: cement, water, CNFs, superplasticizer, fine aggregate, coarse aggregate, and age, and one output parameter: compressive strength f_c. The results declared that seven models for cement paste, six models for cement mortar, and eight models for concrete had a strong ability to predict compressive strength. According to the sensitivity analysis, water and cement were the parameters with the largest impacts on predicting the CNFs reinforced cement‐based composites, while coarse aggregate was the smallest. It can be concluded that Extreme Gradient Boosting Regression, Gradient Boosting Regression, and Random Forest models for concrete; Extreme Gradient Boosting Regression, Gradient Boosting Regression, and Decision Tree models for cement paste; and K‐Nearest Neighbors, Bagging Regressor, and Random Forest models for cement mortar were the best prediction models.

Cellulose nanofibers (CNFs) are promising bio-based reinforcements for cement-based materials, improving microstructure and mechanical performance while supporting sustainable construction. However, experimental compressive-strength testing is costly and time-consuming across multiple mixture proportions and curing ages. This chapter develops a cross-validated machine learning framework to predict the compressive strength of CNF-reinforced cement-based composites using 695 experimental samples, comprising 196 concrete, 266 cement paste, and 233 cement mortar observations. Inputs included cement, water, CNFs, superplasticizer, fine aggregate, coarse aggregate, and curing age, depending on material type. Ten regression algorithms were evaluated, including linear, tree-based, kernel, neighbor-based, boosting, and bagging models. Standardization was incorporated within the modeling pipeline to prevent data leakage, while hyperparameters were optimized using RandomizedSearchCV with 5-fold cross-validation. Performance was further assessed using an independent 70/30 train-test split and repeated 5-fold cross-validation based on R2, MSE, RMSE, MAE, and MAPE. Nonlinear and ensemble models produced the most reliable predictions. For the combined dataset, bagging regression achieved the best test performance (R2 = 0.827, RMSE = 5.698 MPa, MAE = 3.838 MPa) and a cross-validation R2 of 0.810. Random forest performed best for concrete (R2 = 0.867; RMSE = 4.978 MPa), gradient boosting for cement paste (R2 = 0.700; RMSE = 8.500 MPa), and bagging for cement mortar (R2 = 0.724; RMSE = 4.315 MPa). Permutation importance showed that cement, water, curing age, CNFs, and aggregates contributed differently across material types. The framework enables rapid strength estimation and preliminary mixture design.