Abstract
• Extensive model optimizations and performance evaluations are carried out. • XGBoost predicts the chloride migration coefficient with the highest accuracy. • XGBoost outperforms other five tree-based algorithms. • Fresh density and strength tests are predictors of the chloride migration coefficient. • The models can replace traditional laboratory-based concrete chloride migration tests. This work adopts a state-of-the-art machine learning algorithm, XGBoost, to predict the chloride migration coefficient (D nssm ) of concrete. An extensive database of experimental data covering various concrete types is created by gathering from research projects and previously published studies. A total of four D nssm models are developed depending on the number and type of input features. All models are verified with unseen data using four statistical performance indicators and compared to other five tree-based algorithms. The verification results confirm that the XGBoost model predicts the D nssm with high accuracy. The model has the potential to replace cumbersome, time-consuming and resource-intensive laboratory testing.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.