International audienceIn common with most infrastructure systems, sewers are often inspected visually. Currently, the results from these inspections inform decisions for significant investments regarding sewer rehabilitation or replacement. In practice, the quality of the data and its analysis are not questioned although psychological research indicates that, as a consequence of the use of subjective analysis of the collected images, errors are inevitable. This article assesses the quality of the analysis of visual sewer inspection data by analysing data reproducibility; three types of capabilities to subjectively assess data are distinguished: the recognition of defects, the description of defects according to a prescribed coding system and the interpretation of sewer inspection reports. The introduced uncertainty is studied using three types of data: inspector examination results of sewer inspection courses, data gathered in day-to-day practice, and the results of repetitive interpretation of the inspection results. After a thorough analysis of the data it can be concluded that for all cases visual sewer inspection data proved poorly reproducible. For the recognition of defects, it was found that the probability of a false positive is in the order of a few percent, the probability of a false negative is in the order of 25%

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Published online: 12 Sep 2013International audienceOne key aspect of sewer inspection programs is the prediction of sewer condition. Despite the development of deterioration models, the influence of available data on models' predictive power has not been studied in depth. In this article, numerical experiments on a semi-virtual asset stock have been conducted to answer two main questions: how to establish a list of the most informative factors and whether it is better to have data imprecision instead of data incompleteness in a utility database. Two approaches for establishing a list of the most informative factors are compared. The results show a statistical analysis (a priori analysis) can predict the impact of available data on inspection program efficiency (a posteriori analysis). This can be used to plan data acquisition programs. Finally, we show that using the notion of "district" (data imprecision) can provide efficient results when the most informative factor "age" is not available (data incompleteness)