The aim of this research is to create a risk based framework to prioritise proactive investment for sewers in England and Wales. This research proposes a sewer deterioration model that will enhance and not replace the industry’s business as usual process and it also recommends how standard sewer assessment reports can be better utilised to inform business decisions.
The methodology used to complete this research project is a mixture of qualitative and quantitative approaches to analyse a total length of 24,252 km which represents 703,156 records of historic sewer structural condition inspection data. This was used to build an improved deterioration model. Proactive investment (future condition prediction) assessments have been made within Thames Water and other wastewater utilities in the UK. The approaches are reviewed, compared, limitation identified and a robust approach was defined, devising means to mitigate the limitations identified. Existing approaches within and outside the industry to assess sewer condition and model sewer deterioration for risk management was reviewed. Data analytical software such as MATLAB and Tibco Spotfire were used to create an intuitive risk framework that will aid sewer investment decision making.
An improved deterioration model and inspection frequencies for sewers were developed as a premise for proactive investment. This deterioration model and the inspection frequencies were then used to create a risk based framework to help set proactive priorities for sewer management. This would enable sewerage asset owners with large kilometres of sewers to manage the sewerage system more proactively before they reach a critical point and reduce the reliance on industry expert judgement and further surveys. The improved deterioration model and inspection frequencies provided in this research would enable sewer asset managers to determine the most cost-effective time to invest in repairs or replacement. Also, a plausible and reliable validation that was provided would give a high level of confidence in the risk based framework
