H. Korving

The consistency of visual sewer inspection data

In 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%.

Bayesian Estimation of Return Periods of CSO Volumes for Decision-Making in Sewer System Management

Decisions on the rehabilitation of a sewer system are usually based on a single computation of CSO volumes using a time series of rainfall as system loads. A shortcoming of this method is that uncertainties in knowledge of sewer system dimensions are not taken into account. Besides, statistical uncertainties are left aside. This paper presents the effect of variations in sewer system dimensions on return periods of calculated CSO volumes. As an example the sewer system of ‘De Hoven’ (the Netherlands) is used. CSO volumes per storm event are computed using Monte Carlo simulations with a reservoir model of the sewer system. In each Monte Carlo run random values for the sewer system dimensions are drawn and substituted in the model. With regard to the computed CSO volumes probability distributions are estimated taking into account the statistical uncertainties involved. For this purpose so-called Bayes factors are used to determine weights that describe how well a probability distribution fits the computed data, i.e. the better the tit, the higher the weighing. With the fitted probability distributions the 95% uncertainty intervals of calculated CSO volumes and their corresponding return periods are computed. The results show that uncertainties in knowledge of sewer system dimensions cause a considerable variability in return periods of calculated CSO volumes.

Bayesian updating of a prediction model for sewer degradation

Sewer degradation is mainly a stochastic process. The future condition of sewers can be predicted using models based on condition states. In The Netherlands, the SPIRIT model is being developed combining expert opinion and visual inspections. In this model the likelihood function of condition states is updated with inspections. A Dirichlet distribution is used to describe ‘subjective’ prior knowledge, i.e. expert knowledge. The results show that the model can be solved analytically reducing calculation time. In addition, the weight of experts and inspections is determined on the basis of prior information and data instead of estimated by subjective expert knowledge.

Risk-based design of sewer system rehabilitation

Risk and uncertainty are often not taken into account in decision-making on sewer rehabilitation. However, the assessments on which the decisions are based are considerably affected by uncertainties in external inputs, system behaviour and impacts. This is a problem of growing significance. Many sewer systems need expensive rehabilitation due to the deterioration in their performance brought about by changes in inputs, such as urbanization, urban renewal and climate, as well as decay of sewer infrastructure. Rehabilitation should be efficiently designed and implemented, and should also be effective with the objectives of minimizing costs and maintaining safety and reliability. In this article, a risk-based approach is presented, considering uncertainty in sewer system dimensions, natural variability in rainfall and uncertainty in the cost function describing environmental damage. In particular, the application of different shapes of cost functions is studied. The optimization method is illustrated with a case study on optimizing the storage capacity of a sewer system to balancing investment cost and damage due to combined sewer overflows.

Impact of sewer condition on urban flooding: an uncertainty analysis based on field observations and Monte Carlo simulations on full hydrodynamic models

In-sewer defects are directly responsible for affecting the performance of sewer systems. Notwithstanding the impact of the condition of the assets on serviceability, sewer performance is usually assessed assuming the absence of in-sewer defects. This leads to an overestimation of serviceability. This paper presents the results of a study in two research catchments on the impact of in-sewer defects on urban pluvial flooding at network level. Impacts are assessed using Monte Carlo simulations with a full hydrodynamic model of the sewer system. The studied defects include root intrusion, surface damage, attached and settled deposits, and sedimentation. These defects are based on field observations and translated to two model parameters (roughness and sedimentation). The calculation results demonstrate that the return period of flooding, number of flooded locations and flooded volumes are substantially affected by in-sewer defects. Irrespective of the type of sewer system, the impact of sedimentation is much larger than the impact of roughness. Further research will focus on comparing calculated and measured behaviour in one of the research catchments.

Calibration of hydrodynamic model-driven sewer maintenance

Abstract Sewer performance is typically assessed using hydrodynamic models assuming the absence of in-sewer defects. As a consequence, hydraulic performance calculated by models is likely to be overestimated, while the real hydraulic performance of the sewer system remains unknown. This article introduces the concept of ‘hydraulic fingerprinting’ based on model calibration to identify in-sewer defects affecting hydraulic performance. Model calibration enables detection of changes in hydraulic properties of the sewer system. Each model calibration results in a set of model parameter values, their uncertainties and residuals. The model parameter values also incorporate the antecedent condition of the catchment of the event calibrated and are therefore less suitable to identify in-sewer defects. The residuals on the other hand, and more specifically their absolute values, statistical properties and the correlation between residuals at different monitoring locations are suitable as indicators of the occurrence of in-sewer defects. This allows the application of ‘hydraulic fingerprinting’ based on model calibration, where the ‘fingerprint’ is defined by the model parameters and the residuals. The concept of ‘fingerprinting’ is demonstrated for the combined sewer system ‘Tuindorp’ (Utrecht, the Netherlands). The results show that ‘hydraulic fingerprinting’ can be a powerful tool for directing sewer asset management actions.