Deterioration modelling has been developed in the last decades to support operators and municipalities in defining mid-long term asset management strategies with limited availability of sewer condition data (CCTV). Modelling can help validating and showing the viability of current strategies or provide information to justify the relevance of additional investments and expenditures. Several modelling approaches are now available but not commonly used by sewer operators and municipalities to support strategies mainly because of the lack of real scale demonstration of the tangible benefits provided. Indeed, most of these models fail to show that they can adequately forecast future conditions (Ana and Bauwens, 2010; Scheidegger et al., 2011; WERF, 2012).

Deterioration modelling can be a powerful tool to support utilities in planning efficient sewer rehabilitation strategies. However, the benefits of using deterioration models are still to be demonstrated to increase the confidence of utilities toward simulation results. This study aims at assessing the performance of a statistical deterioration model to estimate the current condition and predict the future deterioration of the network. The quality of prediction of the deterioration model GompitZ has been assessed using the extensive dataset of 35,826 inspections of the city of Braunschweig in Germany. The performance of the statistical model has been compared with the performance of a simple model based only on the condition of observed sewers. Results show that the statistical model performs much better than the simple model for simulating the deterioration of the network. The findings highlight the relevance of using modelling tools to simulate sewer deterioration and support strategic asset management.

Im Rahmen des Forschungsprojekts SEMA ist die Prognosequalität eines Alterungsmodells anhand der TV-Inspektionsdaten der Stadt Braunschweig geprüft worden. Die Qualität der Prognose wurde auf der Grundlage einer Probe von 35.826 Inspektionen bewertet. Die Inspektionen wurden mittels eines substanzbasierten Modells klassifiziert. In einem zweiten Schritt wurde das statistische Modell KANEW-Z angewandt, um die Kanalalterung zu simulieren. Der Vergleich der Inspektions- mit den Simulationsergebnissen zeigt, dass das Modell in der Lage ist, die Zustandsverteilung des Systems ziemlich genau wiederzugeben. Die Ergebnisse sind auch ermutigend auf individueller Haltungsebene. Im Allgemeinen zeigt das Alterungsmodell viel bessere Ergebnisse als ein einfaches lineares Alterungsmodell. Schlussfolgernd unterstreichen die Ergebnisse das Interesse und den potentiellen Nutzen der Anwendung von Alterungsmodellen zur Unterstützung von Asset-Management-Strategien.

Several infrastructure studies highlight the ongoing deterioration of critical assets in water and wastewater systems (WERF, 2007). A recent survey among 397 water and wastewater industry participants in the U.S.A. and Canada highlights that aging infrastructure and the management of capital and operational costs are the two main industry issues (Black and Veatch, 2013). From the participants, more than 70% of municipalities and utilities have already implemented condition assessment and inspection programs to assess the condition state of their systems. However, less than 10% are currently using simulation tools to support their asset management strategies. These results underline the strong opportunity for municipalities and utilities to increase the efficiency of their asset management programs by extracting the value of their (already) available data. Several modeling approaches are now available but not commonly used by sewer operators to support strategies (Caradot et al., 2013). Indeed, most of these models still fail to show that they can adequately forecast future conditions (Ana and Bauwens, 2010; Scheidegger et al., 2011). This article presents an assessment of the ability of sewer deterioration models to simulate the condition distribution of sewer networks. The analysis has been done using the extensive CCTV dataset of a German city, Braunschweig.

This article presents an assessment of the quality of prediction of a Markov-based statistical sewer deterioration model using the extensive CCTV dataset of a German city, Braunschweig. Additionally, a sensitivity analysis has been performed in order to assess the influence of input data availability on model performance. Results indicate that models are able to simulate quite accurately the condition distribution of the network with deviations smaller than 1%. Results also indicate that the performance of deterioration models is quite independent of the amount of CCTV data available to calibrate the model. Even when using very few data (˜3%, i.e. 1000 inspections) to calibrate the model, very good model performance can be obtained.This article presents an assessment of the quality of prediction of a Markov-based statistical sewer deterioration model using the extensive CCTV dataset of a German city, Braunschweig. Additionally, a sensitivity analysis has been performed in order to assess the influence of input data availability on model performance. Results indicate that models are able to simulate quite accurately the condition distribution of the network with deviations smaller than 1%. Results also indicate that the performance of deterioration models is quite independent of the amount of CCTV data available to calibrate the model. Even when using very few data (˜3%, i.e. 1000 inspections) to calibrate the model, very good model performance can be obtained.

Asset management is an increasing concern for wastewater utilities and municipalities. Sewer deterioration models have been developed by research and municipalities to support the definition of cost-effective inspection and rehabilitation strategies. However, the acceptance of deterioration models among sewer operators and decision makers still raise considerable challenges. This article presents the state of the art of condition classification and sewer deterioration modeling and discusses key issues for the future development of deterioration models. Research is needed (i) to identify the most appropriate approaches for condition classification and deterioration modeling and (ii) to conclude clearly about their quality of prediction. Due to the high costs associated with CCTV inspection and data collection, the influence of input data on modeling quality and the optimal input data requirement are still to be evaluated. The ongoing project SEMA aims precisely to assess the suitability of models to simulate sewer deterioration. Objectives and strategy are shortly presented at the end of the article.

Recent infrastructure studies underline the general deterioration of sewer systems and the risk reversing public health, environment and increasing costs (ASCE, 2009). Aging pipes have not been inspected, replaced or rehabilitated rapidly enough to prevent sewer deterioration and increasing system failures (Tuccillo et al., 2010). According to a need survey conducted by EPA (2008), total funding needs for replacement, rehabilitation and expansion of existing collection systems for a 20 year period in the USA is 82.7 billions $, i.e. 28% of the total need of public agencies for wastewater treatment and collection. In the last 30 years, most municipalities have invested in sewer system expansion and treatment plant upgrade but a relatively small component has been allocated to the improvement of sewer system condition.