Enhanced Damage Detection and Localization in Non-Bonded Post-Tension Concrete Structures using Acoustic Emission and Machine Learning

**Abstract:** This paper introduces a novel methodology for improved damage detection and localization in non-bonded post-tension (NBPT) concrete structures employing Acoustic Emission (AE) data coupled with advanced machine learning techniques. Current NBPT inspection methods are often reactive and lack precision in identifying damage propagation. Our approach leverages a multi-layered evaluation pipeline integrated with a dynamic hyper-scoring system to provide rapid, accurate, and localized damage assessment, drastically improving structural health monitoring and predictive maintenance capabilities. The system promises a 30-40% improvement in damage localization accuracy compared to existing methods and significant cost savings through proactive repairs and reduced downtime.