Physics-guided machine learning with an early-age durability fingerprint for GFRP long-term evaluation

Key Points

• Durability fingerprint emergence is linked to machine learning applications, enhancing evaluation accuracy.
• Key evidence shows that machine learning can significantly improve the understanding of GFRP longevity, suggesting a novel approach for materials evaluation.
• This analysis uses physics-guided methodologies, enabling advanced insights into the performance of GFRP over extended periods and under varying conditions.
• May enable more reliable long-term evaluations, although further field validation is necessary for broader applicability.