ML models for predicting compressive strength of concrete containing various fibers types

The inclusion of fibers in concrete mix significantly changes the functionality and strength properties of hardened concrete. However, it is quite challenging to determine the specific contribution of fiber in concrete particularly, because of their non-uniform dispersion, and wide variation of material properties compared to that of the actual concrete ingredients. This study investigates the complex domain of predictive modeling for compressive strength of fiberized concrete (FBC), utilizing data-driven fourteen machine learning (ML) techniques. A comprehensive dataset comprising 608 test results of fiberized concrete properties is meticulously collected, organized, and utilized for the training and evaluation of ML models. The input features of the proposed ML models are water-to-cement ratio (W/C), coarse aggregate-cement ratio (CA/C), fine aggregate-cement ratio (FA/C), admixture utilization, percentage of fiber, types of fiber (nine different types), fiber aspect ratio (l/d), and fiber tensile strength (MPa). This study employs a diverse range of fourteen different ML algorithms, including Linear Regression (LR), Ridge Regression (RR), Lasso Regression (Lasso), Decision Tree (DT), Extra Trees Regression (ET), Random Forest (RF), Support Vector Regression (SVR), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Adaboost (AB), Catboost (CB), Gradient Boost (GB), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM). The findings indicate that the XGB model demonstrates the best level of accuracy in predicting the compressive strength of FBC by achieving over 70% of the test data points with minimal error. The feature importance and SHAP value revealed that apart from the W/C ratio, CA/C, and FA/C, the fiber category and its tensile strength (MPa) were identified as crucial parameters that have a substantial effect on the compressive strength of FBC. The analysis also claimed the presence of fiber with a high tensile strength (11%) is important for improving the compressive strength rather than its higher volume percentage.