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.

Recent infrastructure studies underline the general deterioration of sewer system and the risk reversing public health, environment and increasing costs (ASCE, 2009). Since the origin of sewer systems in the 19th century, sewers have been installed at different periods using available standards and technologies. Sewer assets have limited service life and it is crucial to assess their condition throughout their life cycles to avoid potential catastrophic failure and expensive emergency rehabilitation due to their deterioration (Hao et al., 2011). This report first presents the wide panel of inspection technologies available to obtain information about sewer defects and condition. Visual inspection (e.g. Closed-circuit television CCTV, zoom camera) appears to be the industry standard for sewer inspection. It provides visual data (images and/or videos) of the internal surface of the pipe. Defects are usually coded manually by the inspection staff according to standard coding methods. In Europe, the current codification system is the normative EN 13508-2 for visual inspection (EN 13508-2, 2011) used by the CEN-Members (European Committee for Standardization). In addition, physical techniques are available that can give further information and details about pipe defects. These techniques do not replace the CCTV inspection but can give deeper insights on the type and severity of defects. Sonar and Lasers enables to analyze pipe geometry and can identify defects such as deflections, cracks, sediments or corrosion. Ultrasonic testing and magnetic flux leakage (MFL) are applied directly on the pipe wall. They enable to measure wall thickness and detect pipe defects such as corrosion, deflections and cracks. Ground Penetrating Radar (GPR) and Infrared Thermography are used from above ground and are useful to locate pipes and identify bedding conditions, voids and leaks. Finally, network wide inspection technologies like smoke testing or Distributed Temperature Sensing (DTS) can locate cross-connections and/or sewer infiltration. The purpose, inspection procedure and limitations of these methodologies are briefly presented. On a second step, this report presents the available classification methodologies developed to interpret automatically visual CCTV inspection reports and evaluate sewer condition. These methodologies enable to transfer the extensive amount of visual inspection data from CCTV inspection into a more easily manageable number, useful to support asset management practices. Most approaches have a similar goal: they aim to rank rehabilitation priorities and support municipalities in the definition of rehabilitation programs. They do not pretend to replace the knowledge and analysis skills of a local expert but can help him to identify rehabilitation priorities. All methodologies provide an overall condition score for each sewer segment or sub-scores for different requirements (e.g. structural and operational condition) or dysfunctions. From the review of available methodologies, two main approaches can be distinguished: priority based and substance based methodologies. For priority based methodologies, the calculation of sewer condition grades is based on the most severe defects, the density of defects and/or the defects length. Condition grades express the priority of rehabilitation, i.e. the emergency of action regarding the probability of failure or collapse. For substance based methodologies, the final score is calculated based on the length of sewer that will be affected by rehabilitation actions. Substance based methodologies do not aim to assess the condition of sewers but rather to rank sewer pipes considering the amount and type of rehabilitation needs: replacement, renovation and repair. Each methodology aggregates and combines sewer defects in a very different way making very hazardous the benchmarking of final scores from different methods. Therefore, municipalities using different evaluation system are not able to benchmark the condition of theirs networks. Finally, the accuracy of the classification results remains a key issue, crucial for the further use of inspection data to support asset management strategies.