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:  The structural state of sewer systems is often quantified using condition classes. The classes are based on the severity of structural defects observed on individual pipes within the system. Here, a survival analysis model was developed to predict the overall structural state of a sewer network based on camera inspection data from a sample of pipes in the system. The convolution product was used to define the survival functions for cumulative staying times in each condition class. An original calibration procedure for the sewer deterioration model was developed to overcome the censored nature of data (left censored and right censored) available for the calibration of sewer deterioration models. The exponential and Weibull functions were used to represent the distribution of waiting times in each deterioration state. Cross‐validation tests showed that the Weibull function led to greater uncertainty than the exponential function for the simulated proportion of pipes that are in a deteriorated state. Using various sample sizes for model calibration, these cross‐validation tests also showed that the model's results are robust to smaller calibration sample sizes. This confirms the model's potential for predicting the overall state of deterioration of a sewer network when only a small proportion of the pipes have been inspected.

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Combined sewer overflows (CSOs) cause environmental problems and health risks, but poor guidance exists on the use of rainfall data for sizing optimal CSO control solutions. This study first reviews available types of rainfall information as input for CSO modelling and, secondly, assesses the impacts of three rainfall data selection methods (continuous simulation, historical rainstorms selected based on rainfall depth or maximum intensity and IDF-derived storms) on the estimation of CSO volume thresholds to control in order to reach specific seasonal CSO frequency targets. The methodology involves hydrological/hydraulic modelling of an urban catchment in the Province of Québec (Canada). Continuous simulation provides the most accurate volume estimations and shows high sensitivity to the number of simulated *Revised Manuscript with no changes marked Click here to view linked References 2 years. Alternatively, when historical events extracted from rainfall data separated by a minimum inter-event time (MIT) criterion are selected based on their total rainstorm depth, the CSO volumes are underestimated significantly; whereas an analysis based on rainstorm maximum intensities over durations similar to the time of concentration provides more conservative volumes. Finally, synthetic storms constructed from multiple points of an IDF curve tend to underestimate slightly the CSO volumes, but provide acceptable results compared to single point derived storms. It was found that the overflow structures local characteristics had a marginal influence on results obtained from continuous simulation compared to event-based simulation. The use of design rainfall events should thus be restricted to preliminary assessment of CSO volume thresholds, and the final volume estimation for solution sizing should be reviewed under continuous simulation. The innovative contribution lies in the improvement of modelling procedures for solutions design to achieve a maximum CSO frequency, such as specified by many regulating agencies.