High Pressure Cleaning

Sewer main high pressure cleaning justification


Gippsland water sought to understand the relationship between the number of sewer blockages, and the level of spend for their high-pressure sewer cleaning program. Gippsland water engaged Modla to develop a causal model of their sewer blockages, and then use this model to understand the spend relationship.

Various spend levels were considered, and a decreasing rate of return for increased spend was measured.

The sewer main model is available for other utilities to use and improve upon. You can find it on our analytics platformapp.modla.co.

Project objectives

• Review and validate the developed library model with GW engineers and maintainers.

• Document the current thinking for others to see and improve.

• Test the thinking against the current data.

• Use the model to inform future spend levels for pressure cleaning programs.


A causal model was developed outlining Gippsland’s current understanding of how sewer mains perform. Then asset, operational and environmental data was analyzed to see if it fit with the current understanding. Some data points were deemed “not important”, as the data suggested an insignificant relationship.

The parameters for the causal model were derived, using regression and Weibull distributions. The causal model is shown(below).

High Pressure Cleaning Causal Model

Pressure Cleaning Simulation

Each maintenance area with the highest likelihood of blockage as determined by the model was “cleaned” until all the cleaning budget was used for that year. This process was repeated for 5 years under different budget constraints.


• Cost benefit analysis for six spending scenarios

• Projected blockages for each scenario

• Delivered result sets staged after each year

• Identified the mains that are most suitable for relines and renewals.

• Documented assumptions and data sources

Want to know more?

To discuss this case study, arrange a demo, or to chat to an asset analytics enthusiast, arrange a chat!