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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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

For ensuring microbial safety, the current European bathing water directive (BWD) (76/160/EEC 2006) demands the implementation of reliable early warning systems for bathing waters, which are known to be subject to short-term pollution. However, the BWD does not provide clearly defined threshold levels above which an early warning system should start warning or informing the population. Statistical regression modelling is a commonly used method for predicting concentrations of fecal indicator bacteria. The present study proposes a methodology for implementing early warning systems based on multivariate regression modelling, which takes into account the probabilistic character of European bathing water legislation for both alert levels and model validation criteria. Our study derives the methodology, demonstrates its implementation based on information and data collected at a river bathing site in Berlin, Germany, and evaluates health impacts as well as methodological aspects in comparison to the current way of long-term classification as outlined in the BWD.

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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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UV/Vis spectrophotometers have been used for one decade to monitor water quality in various locations: sewers, rivers, wastewater treatment plants (WWTPs), tap water networks, etc. Resulting equivalent concentrations of interest can be estimated by three ways: i) by manufacturer global calibration; ii) by local calibration based on the provided global calibration and grab sampling; iii) by advanced calibration looking for relations between UV/Vis spectra and corresponding concentrations from grab sampling. However, no study has compared the applied methods so far. This collaborative work presents a comparison between five different methods. A Linear Regression (LR), Support Vector Machine (SVM), EVOlutionary algorithm method (EVO) and Partial Least Squares (PLS) have been applied on various data sets (sewers, rivers, WWTPs under dry, wet and all weather conditions) and for three water quality parameters: TSS, COD total and dissolved. Two criteria (r² and Root Mean Square Error RMSE) have been calculated - on calibration and verification data subsets - to evaluate accuracy and robustness of the applied methods. Values of criteria have then been statistically analysed for all and separated data sets. Non-consistent outcomes come through this study. According to the Kruskal-Wallis test and RMSEs, PLS and SVM seem to be the best methods. According to uncertainties in laboratory analysis and ranking of methods, LR and EVO appear more robust and sustainable for concentration estimations. Conclusions are mostly independent of water matrices, weather conditions or concentrations investigated.

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