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Adaptation of sewer networks using integrated rehabilitation management Franz Tscheikner-Gratl, Christian Mikovits, Wolfgang Rauch and Manfred Kleidorfer

ABSTRACT The urban water structure is aging and in need of rehabilitation. Further, the need to address future challenges (climate change, urban development) also arise lines. This study investigates if it is possible to combine rehabilitation and adaptation measures. To do so, we combined an urban development model, an urban drainage model and a rehabilitation model. A case study of a mediumsized alpine city with a sewer length of 228 km and a population of 125,431 was used to develop and apply this method. A priority model to pinpoint the structures in need of replacement was used. This

Franz Tscheikner-Gratl (corresponding author) Christian Mikovits Wolfgang Rauch Manfred Kleidorfer University of Innsbruck, Unit of Environmental Engineering, Technikerstrasse 13, 6020 Innsbruck, Austria E-mail: [email protected]

model considered a deterioration model, vulnerability estimation and other influences. Further different rehabilitation rates and methods were examined. The urban development model used is a simplistic approach specifically tailored for the field of urban infrastructure management. Climate change is considered in terms of climate change factors. All these different influences together create scenarios for which the construction costs and the flooding volume are estimated and compared. Consequently the aim of this paper was to test to which degree it is possible to reduce urban flooding by adapting those parts of the network which require rehabilitation anyway. In our case study it could be reduced by 5%. Key words

| climate change, deterioration, prioritization, scenarios, urban development, urban drainage

INTRODUCTION In the last decades, in many parts of the world e.g. in central Europe, the main challenge for operators of urban drainage systems has moved from connecting new areas to the maintenance and rehabilitation of the existing infrastructure. For example, in Austria more than 90% of the population is connected to sewer systems (Überreiter et al. ). The deterioration of sewers becomes a more and more important issue especially due to the fact that our actual rehabilitation rates are far too low (e.g. 0.07% for Austria in 2012 (Breindl )). Various deterioration and rehabilitation models exist for the estimation of the expected lifetime of sewers (Fenner ). The models can be divided into different groups. The first group are the classical cohort survival models with different forms and enhancements (Hörold & Baur ; Baur & Herz ; Baur et al. ; Duchesne et al. ; Egger et al. ). The next group are the regression models ranging from binary logistic regression which is used in this paper (Ariaratnam et al. ; Davies et al. ; Ana et al. ; doi: 10.2166/wst.2014.353

Fuchs-Hanusch et al. ; Ahmadi et al. ) to ordered logistic regression (Younis & Knight ). Markov-Chain models (Le Gat ) and neural networks (Tran et al. ) are also used. A changing environment can also influence performance of drainage systems and require an adaptation of existing systems. For stormwater and wastewater collection systems, the factors with most influence are climate change and land use change caused by urban development (Semadeni-Davies et al. ; Mikovits et al. ). These changes can put severe pressure on the existing infrastructure but also open a window of opportunity for implementing more sustainable management strategies. Decentralized solutions, as proposed by Smith (), could also help to deal with this challenge by disconnecting paved areas from the main sewer network. Semadeni-Davies et al. () showed that the use of sustainable urban drainage systems and stormwater disconnection from combined sewers could reduce the influence of climate

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change to a low level. Kleidorfer et al. () and Urich et al. () have shown that increased rainfall intensities can to some extent be compensated by the reduction of impervious areas if the necessary adaptations can be implemented (e.g. infiltration systems). The redesign of the urban area can therefore be a valuable option to deal with the influences of climate change and urban development. However, a feasible route in adaptation planning has to combine different measures. Therefore traditional adaptation measures such as the increase of pipe diameters also have to be considered and evaluated. As such, the aim of this paper is to present the application of an integrated approach combining urban development, climate change impact and sewer deterioration models, and hydrodynamic simulations. The idea of this study is to test a drainage system’s potential to combine rehabilitation and adaptation measures i.e. to investigate if it is possible to adapt a system for future conditions by only investing in infrastructure with pressing rehabilitation needs to aid the redesign process of our urban drainage systems.

METHODS Case study and scenarios A case study of a medium-sized alpine city with a sewer length of 228 km and a population of 125,431 inhabitants was used to develop and apply this method.

Table 1

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A total of 48 different scenarios were examined, differing in applied rehabilitation rate, the rehabilitation method and the influence of urban development and climate change. The performance of these scenarios, which are displayed and numbered for identification in Table 1, was assessed using the Storm Water Management Model (SWMM) software tool (Gironás et al. ). The four baseline scenarios represent the current system status without rehabilitation. For this study, the flooded volume of the total network was estimated for all scenarios as well as the costs of the different rehabilitation rates and methods. For the hydrodynamic calculation, a design storm event ‘Euler Type II’ with a duration of two hours and a return period of 5 years was used. This is a common assumption for the design of new and rehabilitated sewers in city centres according to Austrian guidelines (ÖWAV-Regelblatt  ). Rehabilitation scenarios The maximum rehabilitation rates range from 0.5 to 2.0% of the total network length. In reality these rates are highly dependent on the budgetary situation and are therefore seldom reached (although a yearly rehabilitation rate of 1.0% would still require a lifetime of 100 years for our sewers). Because of the high impact of costs on the rehabilitation management, this study examines three different methods for rehabilitation regulated by Austrian guidelines (ÖWAV-Regelblatt  ). These rehabilitation scenarios

Examined scenarios and its identification numbers

Climate change Yes

No

Urban development

Urban development

Rehabilitation rate

Rehabilitation method

Yes

No

Yes

No

Max. 0.5%

Sliplining CIPP Open cut

1.1 1.2 1.3

2.1 2.2 2.3

1.4 1.5 1.6

2.4 2.5 2.6

Max.1.0%

Sliplining CIPP Open cut

3.1 3.2 3.3

4.1 4.2 4.3

3.4 3.5 3.6

4.4 4.5 4.6

Max. 1.5%

Sliplining CIPP Open cut

5.1 5.2 5.3

6.1 6.2 6.3

5.4 5.5 5.6

6.4 6.5 6.6

Max. 2.0%

Sliplining CIPP Open cut

7.1 7.2 7.3

8.1 8.2 8.3

7.4 7.5 7.6

8.4 8.5 8.6

9.3

9.2

9.4

9.1

Baseline scenario 9 ¼ Current state

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are not entirely realistic, however, to allow an easy and automatic computation of the scenarios and avoid a mixture of methods which occurs due to the individual conditions (e.g. failure type, surface, etc.) surrounding every rehabilitation project. Implementing all these factors into this study would require better data. The scenarios were mainly chosen to demonstrate the influences of changes in diameter and roughness due to rehabilitation. Methods such as burst lining, which would elicit the same qualities but would only differ by construction costs (at least in this model) were not considered. The methods considered were as follows. (a) The sliplining method: in this method, new pipe (mainly High density polyethylene (PE-HD) and polypropylene (PP)) is pulled into the existing sewer by means of a winch. The main disadvantage is the resulting reduction of the sewer cross-section area. For our scenarios we assumed that the diameter is reduced by 50 mm to DN500 (500 mm) and above this threshold by 100 mm. Sewers with a diameter of less than 200 mm allow no reduction and therefore method (b) is applied. (b) Cured in place pipe (CIPP): for this method, a hose saturated with resin is inserted into the sewer, pressed to the sewer walls with air or water pressure and cured in place by heat or UV radiation. The reduction of the cross section is therefore minimized and for our study we assumed it to be negligible for the hydrodynamic calculation. This can only be assumed if the structural condition of the sewer allows it and a non-constructive relining can be used. Therefore sewers with structural deficits might not be suitable for this rehabilitation method. For this study however, due to the lack of detailed condition data, we chose to neglect this limitation. Further we assume that the roughness of the rehabilitated sewers for both sliplining and CIPP decrease and a Manning roughness of 0.011 for smooth plastic pipes was applied (Rossman ). (c) The open cut method: this classic third method examined allows the usage of pipes with a higher diameter. To estimate those pipes which would need a higher diameter under future conditions, we calculated the examined scenarios for the end of our observation horizon in the year 2030 including the used factors (climate change and urban development). We highlighted those sewers that reached their hydraulic capacity for more than 1 hour. For these conduits the diameter was expanded by 100 mm in case of rehabilitation. This assumption is conditional on the type of design storm used. As the storm event was chosen

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according to design guidelines, this accords with a realistic planning procedure and enables us to benefit from hydrodynamic model results and consideration of nonsteady effects during flooding and surcharge conditions. Climate change scenarios As a climate change scenario, an increase in rainfall intensities was considered. The assumed climate change factor of 1.3 for a 10-year return period proposed by Arnbjerg-Nielsen () was used to estimate the rainfall intensity in 2030. For the period between the starting year (no climate change effect) and 2030, linear interpolation of the climate change factors was used (Urich et al. ). Costs The costs of the different methods were estimated using the information of Zhao & Rajani () who recorded the diameter dependent costs of each method per m. These data were converted into Euro and multiplied with the construction cost index (Statistik Austria ) to get a plausible cost function as shown in Figure 1. It can be observed that the open cut method gets to be financially rewarding with higher pipe diameters. Priority model The priority model to select the rehabilitated sewers for this case study uses a simple additive weighting method (Tzeng & Huang ). It was selected due to its simplicity and intuitive usage. The linear additive function represents the preferences of decision makers under the assumption of

Figure 1

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Costs of different rehabilitation methods from data in Zhao & Rajani (2002).

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rational decision making and meeting the required assumption of preference independence or preference separability (Tzeng & Huang ). It consists of four parts: (1) a deterioration model (PDet); (2) a vulnerability analysis (PVul); (3) the importance of the affected street (PIS); and (4) an urban development model (PUD), which is not included in all scenarios (compare Table 1). These parts were weighted (W ) and summed up to create a priority number for the individual pipe using the following formula:

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Table 2

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Significant factor for the used deterioration model

Coefficients Significant factors

β

Standard errors

Wald statistics

Significance levels

Pipe shape (circular)

0.574

0.234

6.017 0.014

Pipe age

0.023

0.002

132.250 0.000

Pipe diameter

0.002

0.000

23.329 0.000

Pipe slope

0.006

0.002

10.500 0.001

0.020

0.003

43.957 0.000

Pipe length

P ¼ jPDet j  WDet þ jPVul j  WVul þ jPIS j  WIS þ jPUD j  WUD The weights depend on the objectives of the operating company. For this study it was assumed that deterioration has the highest influence on prioritization (WDet ¼ 0.5), while the other factors have a minor influence (WVul ¼ 0.25 and WIS ¼ WUD ¼ 0.125).

Deterioration model For the deterioration model a binary logistic regression model (see Ana et al. () and Ahmadi et al. () for detailed information) with a logit link was used. The estimation of the condition states was based on visual sewer inspection (in which 70% of the network was inspected) distinguishing 5 states ranging from immediate action necessary (CS5) to no action necessary (CS1), following ISYBAU coding (BMVBS ). The two condition states that require the highest attention (CS4 and CS5) were summed up into one group (not acceptable condition) and the rest of the condition states (CS1 to CS3) into another group (acceptable condition) to form a binary variable. One of the main advantages of logistic regression analysis is the fact that it does not make any assumptions about the distributions of the explanatory variables (Ana et al. ). For this binary response variable, the logistic regression model estimates the conditional probability of observing the outcome ‘not acceptable condition’ (for our model, used as PDet), given the regressors. There are several statistical methods to form the model. We built the model using the stepwise forward method (Fuchs-Hanusch et al. ). Starting with pipe age, we added a new covariate from the available data (pipe age, diameter, slope, length, shape, material) in every step to form the model. We compared every model with the anterior using the likelihood ratio test (Ana et al. ) until no significant improvement was visible. Table 2 displays the significant regressors and the

affiliated statistical properties – coefficients (estimated using the maximum likelihood estimation), standard errors, Wald statistics and significance levels. The significant regressors used are therefore pipe age, diameter, slope, length and a distinction if the pipe has a circular shape or not. To consider the aging system over the examined period we used the model to predict the probability every year using the non-rehabilitated sewers with its real pipe age. Ana et al. () and Fuchs-Hanusch et al. () also found the different materials to be significant but in our network this could not be verified (with a significance level of 0.128 and no significant improvement in the model with material inclusion in the likelihood ratio test, although an odds ratio of 2.66 between concrete pipes and others was observed) due to the large amount of concrete pipes (73.68% of the total network) and no existing brick sewers. On the other hand, in our study, pipe diameter has an influence. Pipes with higher diameters deteriorate more slowly. This was also observed by Davies et al. (), Ariaratnam et al. (), FuchsHanusch et al. () and Ahmadi et al. (). Also, in our study pipes with a steeper slope deteriorate slower, which was also observed by Hörold & Baur (), and circular shaped pipes deteriorate faster, as noted by Baur & Herz () and Fuchs-Hanusch et al. (). Sewage type, which was also a significant influence for Fuchs-Hanusch et al. () and Ahmadi et al. (), was not implemented here because the network is an entirely combined system. Figure 2 shows the receiver operating characteristic (ROC) curves of the model used. We can see that the fit of the model is reasonable (area under the ROC-curve 0.811). For comparison, the model only using pipe age (area under the ROC-curve 0.763) and that using all variables including material (area under the ROC-curve 0.810) are also shown. We can also see here that including material offers no real improvement in the model. To reach a sensitivity and specificity of around 0.75, a cut-off value

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Figure 2

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Vulnerability and importance of streets The vulnerability of the network was estimated by using the Achilles approach described by Möderl et al. (). From this approach we derive the effect of the collapse of a pipe on flooding in the whole network (Figure 3). This value is normalized and used for the prioritization model (PVul). The importance of the affected streets (i.e. the streets where sewers in need of rehabilitation are situated) is

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ROC-curves (left) and sensitivity/specificity plot (right).

between the binary variables of 0.13 is estimated by using the sensitivity/specificity plot.

Figure 3

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Vulnerability to pipe collapse of the case study.

assessed by using the street types used by Open Street Map (see www.openstreetmap.org), prioritizing them from motorway (PIS ¼ 100; roads with high importance) to path (PIS ¼ 10; with low importance). Urban development To simulate urban development the DynaMind simulation framework (Urich et al. ), a scientific workflow engine with focus on dynamics, was used to read the input data and simulate parcelling of areas. The simulation of urban development was made using a dynamic model

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described in Mikovits et al. (). Parcels are generated within given boundaries, buildings are placed onto the parcels and population is distributed based on population predictions on suitable areas which are determined by distance calculations. These steps are followed by the calculation of relevant parameters for urban water services like dry weather flow, peak runoff coefficient and water demand. These steps are repeated for a fixed amount of time where one cycle represents 5 years in reality. At the end of each loop, a polygon-shape file with attributes according to the simulation set-up is generated for further usage. Since 2000, governmental regulations have enforced the application of infiltration methods on newly erected buildings. Nevertheless, the effective impervious area increases simply due to the fact that there was no sealing at all before (e.g. undeveloped agricultural areas). In the model, urban development and consequently changes in the effective impervious area will happen from 2014 to 2020 when larger parts of the city get developed leading to more and more sealed areas. Until 2030, steady construction occurs although large-scale areas in the city are not available anymore. Overall, the effective impervious area of the case study increases by 8% until 2030. The year of development of an area influences the priority model. For the current year, it would be preferable that rehabilitation methods are carried out in an area, due to already ongoing construction work in that same area. So, PUD for the pipes surrounding the developed area for these years is set to 1, whilst for the 3 previous

Figure 4

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and 3 following years it linearly decreases to 0. The urban development model’s use is limited to changes in the impervious area over the examination period and the direct influence of neighbouring sewers. It cannot simulate urban redevelopment and road reconstruction, which are important aspects of the rehabilitation process.

RESULTS AND DISCUSSION The result of the priority model for the different scenarios was used for the changes of qualities depending on the scenario in the hydrodynamic model. Also, it delivers input for the cost estimation (pipe diameters and length). Figure 4 shows the exemplary result of the priority model for a maximum rehabilitation rate of 2.0% per year considering the effects of urban development. We see that the first rehabilitation measures have to be taken in the city centre while the focus shifts more and more to the suburbs until the end of the observation period. The results for the scenarios of this rehabilitation rate (7.1–8.6 as seen in Table 1) are shown exemplarily in Table 3. Additionally, the average yearly cost for rehabilitation measures at the given rehabilitation rate are displayed. We see that the open cut method (scenarios 7.3 and 8.6) is ca. 2.5 times more expensive than the other two methods, which differ only slightly in terms of costs. In Figure 5 we see the influence of the three rehabilitation techniques on a static system without including any

Result of the priority model for scenarios with a rehabilitation rate of 2.0% considering the influence of urban development.

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Table 3

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Adapting sewer networks using integrated rehabilitation

Results for the exemplary scenarios in 2030

Scenario number

Flooding volume [m³]

Average yearly costs [€]

7.1

217.05

1,640,857

7.2

207.15

1,578,986

7.3

208.13

4,179,343

8.4

99.90

1,623,110

8.5

87.91

1,570,648

8.6

84.83

4,029,862

9.1

95.30



9.3

217.91



influences from urban development or climate change (scenario 9.1). By using the open cut method (scenario 8.6) we can decrease the flooding volume on the static system by 11%; by using the CIPP method (scenario 8.5) it decreases by 7.75%. With sliplining (scenario 8.4) the flooding would increase by 4.8%. If we consider the changes due to urban

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development and climate change (scenario 9.3) we see a slight change. The open cut method (scenario 7.3) and the CIPP method (scenario 7.2) cause the same decrease in flooding (5%) in comparison to the base scenario (scenario 9.3). The sliplining method has no influence at all. If we look at a more plausible rehabilitation rate of 0.5% per year we can observe a different outcome (Figures 6 and 7). Regardless of whether we have an influence from urban development or climate change (scenario 9.3) or not (scenario 9.1), only the open cut method has a significant positive influence. Even with this low rehabilitation rate we can achieve a decrease of 8.02% in flooding volume compared to scenario 9.1, and by 3.50% compared to scenario 9.3. For the two other methods the fluctuations are big; depending on the pipes where the rehabilitation takes place, it can have negative or positive influence on the flooded volume. The positive influence of the CIPP method cannot be reproduced for this low rate due to the fact that only a few pipes were enhanced.

Figure 5

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Increase and decrease of flooding volume for different scenarios in m³ and % referring to the baseline scenario 9.1.

Figure 6

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Increase and decrease of flooding volume for different scenarios in m³ and % referring to the baseline scenario 9.3.

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Figure 7

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Increase and decrease of flooding volume for different scenarios in % referring to the baseline scenario 9.3 and 9.1.

The fluctuations in flooding volume over the years in the scenarios is caused by the replacement of individual pipes, which could cause short-term effects (for example in Figure 6 in 2022). Also, the baseline scenario 9.3 is not a linear increase as could be expected by the linear assumption of the climate change factor, due to the system qualities and geometry of the observed network. The observed flooding volume can be regulated by combined sewer overflows (CSO) in the system and in fact an increase in CSO emissions could be observed. This is, however, outside the scope of this paper.

of restricted budgets, a careful choice of methods by taking into account different scenarios and influencing factors becomes even more important. This paper shows a first trial of the development of integrated rehabilitation models. Further efforts have to be made in order to overcome limitations of the models used (e.g. the priority model and the climate change model) and in application of the methodology for other case studies to strengthen the statement of this paper.

ACKNOWLEDGEMENTS CONCLUSION This paper shows that the necessary rehabilitation of an existing network can contribute, to a certain amount, to the adaptation to a changing environment. But even this small amount should be exploited due to the fact that the rehabilitation has to be carried out anyway and a smaller amount of accompanying measures (e.g. infiltration) has to be constructed and budgeted. The higher costs for open cut rehabilitation compared to trenchless methods cannot be justified in terms of flooding. For higher rehabilitation rates, the performance of CIPP rehabilitation compared to open cut methods is nearly the same at 40% of the costs. For low rehabilitation rates, the effect of all methods stays minor and the higher costs are therefore not justifiable. Although in this case only an expansion of 100 mm for the open cut was allowed and with higher diameters the performance could be enhanced, the usage of open cut methods should be carefully planned and mainly used for higher diameters, customized profiles and easy accessible sewers. In times

This work is part of the project ‘DynAlp – Dynamic Adaptation of Urban Water Infrastructure for Sustainable City Development in an Alpine Environment’ (project number KR11AC0K00206) funded by the Austrian Climate and Energy in the Austrian Climate Research Program and it is part of the project ‘REHAB – Integrated rehabilitation management of urban infrastructure networks’ (project number 832148) funded by the Austrian Research Promotion Agency (FFG).

REFERENCES Ahmadi, M., Cherqui, F., de Massiac, J.-C. & Le Gauffre, P.  Influence of available data on sewer inspection program efficiency. Urban Water Journal 11 (8), 641–656. Ana Jr., E. V., Bauwens, W., Pessemier, M., Thoeye, C., Smolders, S., Boonen, I. & de Gueldre, G.  An investigation of the factors influencing sewer structural deterioration. Urban Water Journal 6 (4), 303–312. Ariaratnam, S. T., El-Assaly, A. & Yang, Y.  Assessment of infrastructure inspection needs using logistic models. Journal of Infrastructure System 7 (4), 160–165.

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Arnbjerg-Nielsen, K.  Quantification of climate change effects on extreme precipitation used for high resolution hydrologic design. Urban Water Journal 9 (2), 57–65. Baur, R. & Herz, R.  Selective inspection planning with ageing forecast for sewer types. Water Science and Technology 46 (6–7), 389–396. Baur, R., Zielichowski-Haber, W. & Kropp, I.  Statistical analysis of inspection data for the asset management of sewer networks. In: 19th EJSW on Process Data and Integrated Urban Water Modeling. Conference Proceedings, IWA (ed.) Breindl, D.  Kanalsanierung eine Herausforderung für die Zukunft (Sewer rehabilitation – a challenge for the future). In: Österreichischer Wasser- und Abfallwirtschaftsverband (ÖWAV) (ed.), Seminar: Sanierung und Anpassung von Entwässerungsmaßnahmen. Alternde Infrastruktur, Landnutzungsänderungen und Klimawandel. ÖWAV, Vienna, Austria. Bundesministerium für Verkehr, Bau und Stadtentwicklung (BMVBS)  Arbeitshilfen Abwasser: Planung, Bau und Betrieb von abwassertechnischen Anlagen in Liegenschaften des Bundes (Working aids for sewers: Design, construction and operation of public facilities in sewer networks). Arbeitshilfen Abwasser 2004/242/D. BMVBS, Berlin, Germany; 1110 pp. Davies, J. P., Clarke, B. A., Whiter, J. T., Cunningham, R. J. & Leidi, A.  The structural condition of rigid sewer pipes: a statistical investigation. Urban Water 3 (4), 277–286. Duchesne, S., Beardsell, G., Villeneuve, J.-P., Toumbou, B. & Bouchard, K.  A survival analysis model for sewer pipe structural deterioration. Computer-Aided Civil and Infrastructure Engineering 28 (2), 146–160. Egger, C., Scheidegger, A., Reichert, P. & Maurer, M.  Sewer deterioration modeling with condition data lacking historical records. Water Research 47 (17), 6762–6779. Fenner, R.  Approaches to sewer maintenance: a review. Urban Water 2 (4), 343–356. Fuchs-Hanusch, D., Friedl, F., Möderl, M., Sprung, W., Plihal, H., Kretschmer, F. & Ertl, T.  Risk and Performance Oriented Sewer Inspection Prioritization. In: World Environmental and Water Resources Congress 2012, ASCE (ed.), pp. 3711–3723. Gironás, J., Roesner, L. A., Rossman, L. A. & Davis, J.  A new applications manual for the Storm Water Management Model (SWMM). Environmental Modelling & Software 25 (6), 813–814. Hörold, S. & Baur, R.  Modelling sewer deterioration for selective inspection planning – case study Dresden. In: Proceedings of the 13th European Junior Scientist Workshop, Technische Universität Dresden (ed.). Kleidorfer, M., Möderl, M., Sitzenfrei, R., Urich, C. & Rauch, W.  A case independent approach on the impact of climate change effects on combined sewer system performance. Water Science and Technology 60 (6), 1555. Le Gat, Y.  Modelling the deterioration process of drainage pipelines. Urban Water Journal 5 (2), 97–106. Mikovits, C., Jasper-Toennies, A., Huttenlau, M., Einfalt, T., Rauch, W. & Kleidorfer, M.  Dynamic adaptation of

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urban water infrastructure in response to a changing environment. In: Novatech 2013. Conference Proceedings, Rhone-Alps Research Group on Infrastructure and Water (ed.). Mikovits, C., Rauch, W. & Kleidorfer, M.  Dynamics in urban development, population growth and their influences on urban water infrastructure. Proceedia Engineering 70, 1147– 1156. Möderl, M., Kleidorfer, M., Sitzenfrei, R. & Rauch, W.  Identifying weak points of urban drainage systems by means of VulNetUD. Water Science and Technology 60 (10), 2507– 2513. ÖWAV-Regelblatt 28. Österreichischer Wasser- und Abfallwirtschaftsverband  Unterirdische Kanalsanierung (Trenchless Sewer Rehabilitation), 41 pp. ÖWAV-Regelblatt 11. Österreichischer Wasser- und Abfallwirtschaftsverband  Abwassertechnische Berechnung und Dimensionierung von Abwasserkanälen (Calculation and Design of Sewers), Vienna, Austria, 96 pp. Rossman, L.  Storm Water Management Model User’s Manual Version 5.0 EPA/600/R-05/040. U.S. EPA, Office of Research and Development, NRMRL, Water Supply and Water Resources Division, 295 pp. Semadeni-Davies, A., Hernebring, C., Svensson, G. & Gustafsson, L.-G.  The impacts of climate change and urbanisation on drainage in Helsingborg, Sweden: combined sewer system. Journal of Hydrology 350 (1–2), 100–113. Smith, B. R.  Re-thinking wastewater landscapes: combining innovative strategies to address tomorrow’s urban wastewater treatment challenges. Water Science and Technology 60 (6), 1465–1473. Statistik Austria  Baukostenindex für den Straßenbau ab Basisjahr 1990, 2000, 2005, 2010, Statistik Austria. https:// www.statistik.at/web_de/statistiken/produktion_und_ bauwesen/konjunkturdaten/baukostenindex/index.html (accessed 14 February 2014). Tran, H. D., Perera, B. J. C. & Ng, A. W. M.  Markov and neural network models for prediction of structural deterioration of storm-water pipe assets. Journal of Infrastructure System 16 (2), 167–171. Tzeng, G.-h. & Huang, J.-j.  Multiple Attribute Decision Making: Methods and Applications. A Chapman & Hall Book. CRC Press, Boca Raton, FL, USA. Überreiter, E., Lenz, K. & Zieritz, I.  Kommunale Abwasserrichtlinie der EU – 91/271/EWG: Österreichischer Bericht 2012 (Urban Wastewater Treatment Directive of the European Union – 91/271/EWG: Austrian Report 2012), Bundesministerium für Land- und Forstwirtschaft, Umwelt und Wasserwirtschaft, Sektion VII Wasser, Vienna, Austria, 30 pp. Urich, C., Bach, P. M., Hellbach, C., Sitzenfrei, R., Kleidorfer, M., McCarthy, D. T., Deletic, A. & Rauch, W.  Dynamics of cities and water infrastructure in the DAnCE4Water model. In: 12th International Conference on Urban Drainage. Conference Proceedings, IWA (ed.). Urich, C., Burger, G., Mair, M. & Rauch, W. 2012 Dynamind: a software tool for integrated modelling of urban environments

1856

F. Tscheikner-Gratl et al.

|

Adapting sewer networks using integrated rehabilitation

and their infrastructure. In: Proceedings of 10th International Conference on Hydroinformatics 2012. Hamburg, Germany (R. P. Hinkelmann, Y. Liong, D. A. Savić, M. H. Nasermoaddeli, K. F. Daemrich, P. Fröhle & D. Jacob, eds). TuTech-Verl, Hamburg, pp. 1–8. Younis, R. & Knight, M. A.  Continuation ratio model for the performance behavior of wastewater collection networks.

Water Science & Technology

|

70.11

|

2014

Tunnelling and Underground Space Technology 25 (6), 660–669. Zhao, J. Q. & Rajani, B.  Construction and Rehabilitation Costs for Buried Pipe with a Focus on Trenchless Technologies 101. Institute for Research in Construction, National Research Council Canada, Ottawa, Ontario, 42 pp.

First received 28 February 2014; accepted in revised form 31 July 2014. Available online 19 August 2014

Adaptation of sewer networks using integrated rehabilitation management.

The urban water structure is aging and in need of rehabilitation. Further, the need to address future challenges (climate change, urban development) a...
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