The performance of lateral house connections has a direct impact on sewer serviceability. Despite the potential consequences of a blockage, these components are generally maintained with a reactive approach. As inspection data on the condition of lateral house connections are scarce, this study adopts a statistical procedure to support proactive strategies by analysing spatial blockage patterns to identify system parts with higher blockage incidences. First, a Monte Carlo simulation test provides insight into whether the spatial variation of the blockage likelihood is significant. This justifies the identification of explanatory factors by means of a bootstrapped generalised additive model. Application of the procedure to two databases containing 10 years of lateral house connection blockage data, revealed factors such as building age, sewer system type and ground settlement rate to explain spatial differences in the blockage likelihood. Furthermore, a likelihood ratio test demonstrated that the addition of a spatial smoother improved model performance. This smoother was able to account for additional spatial variation caused by explaining factors for which no data were available. The procedure provides key information for inspection and rehabilitation strategies by taking into account the model performance in assessing the trade-off between costs and benefits in terms of serviceability.
- blockage database
- generalised additive modelling
- lateral house connection
- modelling spatial variation
- Sewer deterioration