Dramatic voids and how to find them - In-pipe condition assessment of sewer pipes
Hengameh Noshahri is a PhD student in the department Robotics and Mechatronics. Co)Promotors are prof.dr.ir. S. Stramigioli and dr.ir. E.C. Dertien from the faculty of Electrical Engineering, Mathematics and Computer Science and prof.dr.ir. A.G. Doree and dr.ir. L.L. olde Scholtenhuis from the faculty of Engineering Technology.
Sewer pipes are designed to transport sewage and simultaneously convey surface-level loads through the subsoil. Consequently, voids around buried sewer pipes pose a threat to the structural integrity of the pipes and the surrounding ground. Since the sewer system is part of a dense, vast, multi-layer underground network of buried utilities, void formations can have an adverse impact on the functioning of other utilities. Moreover, if these voids are not timely maintained, they can eventually grow into sinkholes, posing safety risks to residents, creating nuisances in public spaces, and incurring capital-intensive reparation costs for asset owners and municipalities.
Sinkholes still frequently catch asset managers by surprise because the currently available inspection methods are incapable of characterizing void formations. The absence of a suitable inspection method results in a reactive approach to this critical condition aspect of sewer pipes. This contradicts prospects of sustainability guidelines and proactive maintenance ambitions.
As part of the TISCALI (Technological Innovation for Sewer Condition Assessment - Long-range Information system) project, this work contributes to informed decision-making in proactive sewer asset management in three main themes. First, a data-needs-based classification of inspection methods is developed, linking these methods to specific condition cues they provide data about. This valuable tool helps sewer asset managers select inspection methods based on the data needs of their local sewer system.
Second, to address the niche in void detection inspection methods, this work offers guidelines for designing in-pipe Ground Penetrating Radar (GPR) surveys. Further, it develops an AI-assisted method to automate the process of characterizing voids of conical and irregular shapes around concrete sewer pipes from the in-pipe GPR survey results. Numerical modelling and experiments offer valuable insights into survey designs for this method and validate its efficiency.
Third, this work develops a void detection method based on stress waves and an in-pipe inspection robot capable of conducting this method inside sewer pipes. Numerical modelling reveals the impact of both pipe and void geometry on the void detection. Experimental results confirm these observations and validate the method's efficacy in identifying voids and variably compacted areas.
The survey results obtained from these inspection methods complement data from other methods, such as CCTV (Closed-Circuit Television) surveys, and help provide a holistic view of sewer conditions within asset management information systems.