Intelligent Crack Detection in Building Structures using Coupled Ultrasonic Guided Wave and Acoustic Emission Sensing

Building structural integrity evaluation is important in the long-term safety and resilience of buildings. Visual inspection techniques that have been in place do not have the capability of detecting beneath surface cracks, or cracks that may develop at an early stage, that would compromise the structural performance. This paper introduces a smart crack sensor with a combination of Ultrasonic Guided Wave (UGW) and Acoustic Emission (AE) as a guide to the complicated Structural Health Monitoring (SHM) of building structures. The system proposed is based on the UGW-based wave propagation analysis along with the AE signal monitoring, which will detect, localize, and characterize surface and internal cracks with the highest accuracy. Algorithms of machine learning are used to comprehend complicated acoustic signals and distinguish between crack initiation, crack propagation, and the noise in the environment. It is experimentally verified on reinforced concrete specimens that the coupled UGW-AE methodology is more sensitive and accurate than the uniaxial methodologies. These are possible through the combination of real-time data acquisition, fusion of signals, and smart pattern recognition that allows early detection of damage and provides the ability to monitor the damage continuously. The study will help in the emergence of an intelligent, non-destructive, and scalable SHM system capable of improving structural dependability and maintenance effectiveness in contemporary infrastructure systems.