This paper approaches the sewerage asset management challenge from a UK perspective by outlining a comprehensive methodology capable of optimising the performance of sewerage infrastructure networks using a series of Hydroinformatic tools. In order to define, evaluate and forecast the future performance of sewerage assets, a unique deterioration model is established to predict the future condition of the network. The model analyses historic CCTV survey information to identify deterioration trends based on key pipe characteristics. Against, this improved understanding of past, current and future condition, a collapse rate is predicted by correlating historic failures against the observed sewer condition profiles. The result is a novel relationship which is drawn between collapse rate and condition profile. From here, a prioritised inspection programme can be delivered that targets poorly performing and high consequence of failure assets. The survey information gather from these studies feeds into a previously successful sewer rehabilitation optimisation model that has been adapted under this new study to provide a mechanism for engineers to evaluate the trade-offs that exist between different sewer rehabilitation schemes. A series of GIS tools have been integrated within the model to identify the benefits from an operational perspective, thus guiding investment decisions towards those assets predicted to be in poor structural condition as well as causing operational issues, i.e., pollution, blockage and/or flooding events. As a result, the methodology acts as a series of strategic decision support tools which is capable of helping sewerage engineers and planners in the evaluation of different intervention programmes of work. A UK case study is provided to demonstrate the benefits of this approach.
Ward, Ben and Savić, Dragan A., "A Novel Decision Support System For Optimized Sewer Infrastructure Asset Management" (2014). CUNY Academic Works.