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.