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One key goal of sewer inspection programmes is to target segments in the worst condition. Despite the development of deterioration models, the influence of available data on models’ predictive power has not been studied in depth yet. In this article, numerical experiments have been conducted to answer three main questions: (1) How can the data most probably available within a utility be used to define an effective inspection programme? (2) Can we use an auxiliary variable in order to compensate effects of missing data on inspection programmes? (3) Is it worth to accept a degree of uncertainty within data instead of not having them? In other words, is it preferable to have uncertainty instead of incompleteness within utility database? In order to respond to these questions, we considered an asset stock and then degraded the information by introducing uncertainty, imprecision and incompleteness within, to form a utility's database. The results show that significant improvement of inspection programmes could be achieved by using the most probably available data within utilities. We also show that using the notion of ‘district’ can provide efficient results when the most informative factor ‘age’ is not available. Finally, it is shown that having uncertain data is preferable to having incompleteness. [ABSTRACT FROM AUTHOR]

One 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 priorianalysis) can predict the impact of available data on inspection program efficiency (a posteriorianalysis). 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). [ABSTRACT FROM PUBLISHER]

Asset management is an increasing concern for the water and wastewater industry. Condition assessment of sewer segments is an important component of sewer asset management and relies mostly on visual inspection. Observed defects are translated into a score for each segment. Although most protocols give a segment a condition grade by comparing its score with a subjective scale of numerical values, we propose a protocol to calibrate thresholds for each asset stock. Thresholds are calculated according to two sets of parameters: overall condition of the asset stock in question (estimated by a representative sample or provided by the utility manager) and assignment-error weighting (determined by the utility manager) linked to either over-estimation or under-estimation of condition grade. This method is applied to 150 km of sewers from the Greater Lyon asset stock. Sensitivity analyses of these parameters are then implemented. Three hypotheses about overall condition of the asset stock are combined with three matrices of assignment-error weights. Both parameters influence thresholds and change the assessment of the studied segments. The synthesis of such sensitivity analyses can be used to prioritise complementary investigations. [ABSTRACT FROM AUTHOR]