Smart condition monitoring of municipal sewer pipe networks - Smart Sewers

Water and wastewater organizations spend billions annually to renew their networks. Efficient renewal strategies for prioritization are crucial to make the most of limited resources. Condition and performance assessment is crucial for renewing and maintaining sewer networks. Conventional methods rely on CCTV inspections, which are time-consuming, labour-intensive, costly and subjective.

Additionally, they lack continuous real-time monitoring capabilities, which limits proactive and predictive decision-making.

This research proposes a smart approach to assessing the condition and predicting the performance of sewer pipes using data from vibration and acoustic sensor measurements.

This approach aims to improve the efficiency and cost-effectiveness of assessing and maintaining municipal sewer networks compared to conventional methods that rely on CCTV inspections.

The research will focus on characterizing baseline conditions in sewer pipes using frequency response functions, and modal data, and identifying patterns or signatures that indicate deviations from the baseline state, such as cracks, infiltration, inflow, and blockages.

The results of this research will enable real-time monitoring of sewer networks aided by machine learning algorithms, facilitating a proactive approach to maintenance and renewal planning.

Vibration and acoustic analysis are expected to offer a more efficient and proactive approach to monitoring and maintaining municipal sewer networks.