Urban drainage systems have developed way beyond the traditional piped combined or separate sewer systems. Many ‘new’ systems are being introduced, ranging from stormwater infiltration facilities to green roofs. However, the widely advocated blue-green infrastructures are typically overlooked by asset managers, which will very likely have detrimental effects on their performance, service life, and wider adoption. In this paper, the working group on Urban Drainage Asset Management (UDAM – https://udam.home.blog/) of the IWA and IAHR Joint Committee on Urban Drainage discusses whether the state-of-the-art knowledge based on conventional sewer asset management is sufficient to develop asset management for blue-green infrastructures (BGIs). The discussion is structured around the five preconditions for effective control and asset management. Results show that asset management for BGIs is still underdeveloped due to a lack of monitoring techniques covering the broad range of BGI benefits and performance indicators, inspection techniques covering relevant failure mechanisms and models describing these mechanisms, maintenance and rehabilitation options, and sufficient support tools to aid inhabitants in the operation and maintenance of their individual BGIs such as green roofs or vegetated swales.

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Sewer asset management gained momentum and importance in recent years due to economic considerations, since infrastructure maintenance and rehabilitation directly represent major investments. Because physical urban water infrastructure has life expectancies of up to 100 years or more, contemporary urban drainage systems are strongly influenced by historical decisions and implementations. The current decisions taken in sewer asset management will, therefore, have a long-lasting impact on the functionality and quality of future services provided by these networks. These decisions can be supported by different approaches ranging from various inspection techniques, deterioration models to assess the probability of failure or the technical service life, to sophisticated decision support systems crossing boundaries to other urban infrastructure. This paper presents the state of the art in sewer asset management in its manifold facets spanning a wide field of research and highlights existing research gaps while giving an outlook on future developments and research areas.@en

Deterioration models can be used to forecast the evolution of the condition of the sewer network under different investment strategies. Models are calibrated using condition scores obtained from sewer visual inspection. Many studies highlighted the uncertainties in the procedure of sewer condition assessment, mainly due to the subjectivity of the coding operator. However, the influence of this uncertainty on the outcomes of deterioration modelling remains unknown. This paper analyses the influence of sewer condition uncertainties on the prediction of deterioration models. An optimisation methodology has been applied to quantify uncertainties in sewer condition assessment from the analysis of a set of repeated inspections. Then, a method is proposed to propagate uncertainties in the survival curves of a deterioration model using the inspection dataset of the city of Berlin, Germany. Results indicate that old pipes in bad condition are more prone to False Negative than False Positive (higher probability to miss defects). As a result, the propagation of uncertainties leads to more pessimistic survival curves with a confidence interval of ±12% at 100 years. The analysis shows that the required replacement rate to maintain a constant network condition is underestimated if uncertainties are not considered.

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Closed Circuit Television Inspection is used since decades as industry standard for sewer system inspection and structural performance evaluation. In current practice, inspection data are helpful to support asset management decisions. However, the quality and uncertainty of sewer condition assessment is rarely questioned. This article presents a methodology to determine the probability to underestimate, overestimate or accurately estimate the real condition of a pipe using visual inspection. The approach is based on the analysis of double inspections of the same sewer pipes and has been tested using the extensive data-set of the city of Braunschweig in Germany. Results indicate that the probability to inspect correctly a pipe in poor condition is close to 80%. The probability to overestimate the condition of a pipe in bad condition (false negative) is 20% whereas the probability to underestimate the condition of a pipe in good condition (false positive) is 15%. Finally, sewer condition evaluation can be used to assess the general condition of the network with an excellent accuracy probably because the respective effects of false positive and false negative are buffered.

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