Cavity detection in tunnel lining structures via scattering source search-based ultrasonic imaging

As a reinforced concrete load-bearing component, tunnel lining is prone to internal cavities between itself and the surrounding rock, which can seriously compromise structural stability and operational safety. Ultrasonic non-destructive testing (NDT) has emerged as a reliable technique for detecting such defects. However, most conventional ultrasonic imaging methods suffer from positioning inaccuracies and prominent imaging artefacts, limiting their practical application. To address this issue, a particle swarm optimisation (PSO)-based approach is proposed, which reformulates the defect imaging task as a scattering source localisation problem. This method regulates search process via a custom-designed fitness function that prioritises signal responses of defects, while suppressing noise and artefact interference in defect-irrelevant regions. Specifically, the fitness function is developed based on probabilistic imaging principles and the signal travel time characteristics of scattered ultrasonic signals, enabling the accurate identification of cavity locations by analysing the population distribution of search agents. Numerical simulations and experimental tests demonstrate that the method achieves precise localisation of cavity defects with varying lengths. The method achieves a minimum length error of 4.35%, which is superior to that of the conventional SAFT method. Overall, this intelligent method provides a robust and efficient solution for structural health monitoring (SHM) in civil engineering.