Predicting Ultra-High-Performance Concrete Compressive Strength Using Tabular Generative Adversarial Networks.

ABSTRACT: There have been abundant experimental studies exploring ultra-high-performance concrete (UHPC) in recent years. However, the relationships between the engineering properties of UHPC and its mixture composition are highly nonlinear and difficult to delineate using traditional statistical methods. There is a need for robust and advanced methods that can streamline the diverse pertinent experimental data available to create predictive tools with superior accuracy and provide insight into its nonlinear materials science aspects. Machine learning is a powerful tool that can unravel underlying patterns in complex data. Accordingly, this study endeavors to employ state-of-the-art machine learning techniques to predict the compressive strength of UHPC using a comprehensive experimental database re

SUBMITTER: Marani A 

PROVIDER: S-EPMC7663629 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature