Development of a machine learning-based graphical user interface for compressive strength prediction of graphene oxide infused concrete

Key Points

• Compressive strength predictions improve significantly when utilizing machine learning techniques, optimizing concrete performance.
• Key evidence includes a 30% accuracy increase in predictions compared to traditional methods, validating this approach.
• Machine learning-based analysis was used to assess the influence of graphene oxide on compressive strength characteristics.
• The findings support the potential for integrating AI into construction materials, although practical valuations are needed.