Speaker
Description
Underground buried pipeline networks play a vital role in sustaining modern urban life by enabling the transportation of essential fluids such as water, oil, and gas. In the UK alone, there are over 300,000 km of clean water mains. However, water leakage in buried pipeline networks is a major challenge due to limited data available on the structural condition and ageing. Much of this loss comes from hidden/background leaks which could be due to small defects such as micro-cracks, corrosion, or wall roughness that remain undetected until they grow into major failures. Non-contact low frequency acoustic methods offer robust and easy to deploy technology capable of detecting in gases or liquids hidden changes in the pipe conditions. In the present work, a perturbation method under low-frequency assumption has been adapted to deal with a waveguide (pipe) problem with localized pit, which resembles the corrosion pits/cracks in walls. The developed methodology is integrated into non-contact pipe condition monitoring tools used in the following related research projects: (i) AI:LINER, an EU multi-institutional project that combines novel acoustic solution, CCTV data, AI-based failure detection, and in-situ monitoring techniques to enhance the asset management life cycle of sewer networks; and (ii) an EPSRC-funded project, to develop bio-inspired micromachine sensors for measuring acoustic quantities.