Estimating the Compressive Strength of Self-compacting Concrete with fiber using an Extreme Gradient Boosting model

Abstract

Self-compacting concrete reinforced with fiber (SCCRF) is extensively utilized in the construction and transportation industries due to its numerous advantages, such as ease of building in challenging sites, noise reduction, enhanced tensile strength, bending strength, and decreased structural cracking. Traditional methods for assessing the compressive strength of SCCRF are generally time-consuming and expensive, necessitating the development of a model to forecast compressive strength. This research aimed to predict the CS of SCCRF using the Extreme Gradient Boosting (XGB) machine learning technique. The research uses the grid search method to optimize the XGB model's hyperparameters. A database of 387 samples is collected in this work, which is also the most enormous dataset compared to those utilized in previous studies. An excellent result (R2 max = 0.97798 for the testing dataset) proves that the proposed XGB model has very good predictive power. Finally, a sensitivity analysis using Shapley Additive exPlanations (SHAP values) is conducted to understand the effect of each input variable on the predicted CS of SCCRF. The results show that the age of samples and cement content is the most critical factor affecting the CS. As a result, the proposed XGB model is a valuable tool for helping materials engineers have the right orientation in the design of SCCRF components to achieve the required compressive strength.