Risk and Performance Oriented Sewer Inspection Prioritization

World Environmental and Water Resources Congress 2012: Crossing Boundaries © ASCE 2012

Risk and Performance Oriented Sewer Inspection Prioritization D. Fuchs-Hanusch1, F. Friedl1, M. Möderl2, W. Sprung3, H. Plihal4, F. Kretschmer4 and T. Ertl4 1

Graz University of Technology, Institute of Urban Water Management, Stremayrgasse 10/I, 8010 Graz, Austria; email: [email protected] 2 Institute of Infrastructure, University of Innsbruck, Technikerstr. 13, 6020 Innsbruck, Austria 3 Holding Graz Services, Wasserwirtschaft, Wasserwerkg. 11, 8020 Graz, Austria 4 University of Natural Resources and Life Sciences, Institute of Sanitary Engineering and Water Pollution Control, Muthgasse 18, 1190 Vienna, Austria Keywords: Risk assessment, inspection planning, logistic regression ABSTRACT In Austria the global financial crisis of the last decade led to major financial problems in the municipalities. The more the municipalities face such difficulties the more it is essential to provide infrastructure operation and maintenance prioritization methods which focus on risks to the public and the environment. A risk assessment methodology, a definition of relevant hazards causing “undesired events” responsible for deficits regarding functional requirements and the use of a bivariate logistic regression analysis to derive the main influencing factors on the occurrence of these hazards is presented in this paper. Further a vulnerability analysis which is used to quantify hydraulic driven consequences of undesired events is described. The methods were applied to one part of an Austrian sewer system and for 2 functional requirements. The results of the analyses are presented according to the hazard “sewer collapse”. INTRODUCTION In the last 40 years 35 billion Euros were invested to design and develop the water supply and wastewater infrastructure systems in Austria (Kainz et al., 2006). Hence one central target in managing municipalities and their infrastructure should be a well planned maintenance of these systems. Nevertheless the buried infrastructure often is neglected according to this target. Possible causes for this negligence are focusing on investments in infrastructures with more visible effects to the public or general budgetary restrictions. The more the municipalities face financial difficulties the more it is essential to provide operation and maintenance prioritization methods which focus on risks to the public or the environment. Currently, a nationwide research project (INFOSAN), funded by the Austrian Ministry of Environment and several major sewer operators, deals with performance oriented sewer operation and maintenance prioritization methods. The European Standard EN 752 (2008) defines 13 functional requirements which have to be

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fulfilled by the sewer system, to document a proper performance Following this standard the parts of the system that are most likely to reduce system performance have to be identified. Therefore it is of further interest to learn about the influences being responsible for undesired events which cause a performance reduction. In this context, this paper deals with “undesired events” due to structural degradation or lack of maintenance. To identify the main influences on structural degradation several statistical models have been derived and verified so far. Davies et al. (2001), Ariaratnam et al. (2001), Ana et al. (2009) or Fuchs-Hanusch et al. (2011) describe the use of regression models for this purpose. To define sewers more likely to fail than others, Davies et al. (2001) statistically investigated the structural condition of rigid sewer pipes. Using a logistic regression analysis the significant influencing parameters on sewer deficiency were derived out of 33 parameters which were expected to be relevant by experts from the UK. Davies et al. (2001) concluded that the use of multiple data sources like CCTV data, soil maps, traffic data or groundwater information which were combined in a spatial overlay (GIS) are the basis for the formulation of risk models for sewer assets. Ariaratnam et al. (2001) further introduced a method, to identify sewers for scheduled inspection, with respect to deficiency probability, based on results of a logistic regression analysis. Ana et al. (2009) also suggested the use of logistic regression to better focus CCTV inspections on pipes which are susceptible to degradation due to statistically derived characteristics. In addition, the aim of this work is to introduce an approach for scheduled inspection and maintenance, which deals with an assessment of risks, comprising the probability of an “undesired event” and the severity of this “undesired event”, with respect to functional requirements. Therefore on the one hand, recorded failure modes identified in CCTV inspection were classified according to their relevance for the defined “undesired events”. On the other hand to include non inspected sewer sections into the risk analysis, the occurrence probability of specific failure modes is of interest. Further the severity of an “undesired event” has to be quantified either due to hydraulic, environmental or to financial consequences. This paper is structured as follows. The applied risk assessment methodology, the definition of relevant hazards causing “undesired events” and the use of a bivariate logistic regression analysis to derive the main influencing factors on the occurrence of these hazards is presented. Further a vulnerability analysis (VulNetUD; Möderl et al., 2009) which is used to quantify hydraulic driven consequences of undesired events is described. So far the method was applied to one part of a project participant’s network and for 2 functional requirements. The results of the analyses are presented according to the hazard “sewer collapse”. MATERIAL AND METHODS Definition of hazards responsible for functional requirements reduction According to EN 752 (2008) the 4 objectives of drain and sewer systems are public health and safety, occupational health and safety, environmental protection and sustainable development. To fulfill these targets the EN 752 (2008) suggests to

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folllow 13 functional requ uirements. Table 1 shows the rellationship bbetween theese requ quirements and a the gen neral objectiives. One requirement r t can relate to more th han onee objective. In worksh hops within the ongoin ng Austrian n research pproject INFOSAN N, 6 functioonal requireements whicch are mostt relevant to o sewer maiintenance and a rehabilitation were w defineed by the paarticipating sewer utiliities. These requiremen nts are: • Protecttion from flo ooding • Not enddangering adjacent a struuctures and utility services • Structuural integrity y and designn life • Maintaaining the flow • Protecttion of grou undwater • Protecttion of surfaace receivinng waters Tab ble 1: Relation nship betweeen objectives aand functional requiremeents (EN 752, 2008)

In furthher workshops conditiion based undesired u ev vents and hhazards whiich are responsiblee for a perfformance reeduction were discussed d and definned. In a miind nal requirem ments were analysed in n detail. Posssible hazarrds mapp all 6 definned function whiich may caause undesirred events and data reequirementss to quantiffy probability andd consequennces were structured with respeect to the functional requiremen nts whiich they afffect. Rissk Assessment Method d To asseess risks ressponsible foor sewer sysstem perform mance reducction a failu ure mode and effe fects (FMEA A) conceptt was adaptted. The aiim is to haave a flexib ble metthod to quanntify risks at a the sewerr level. With h the concep pt of calculaating a failu ure riskk index (FR RI) for each individual sewer this aim can be fulfilled. T The FRI is ded fineed as probabbility (p) off an undesirred event tim mes consequ uence (c) off an undesirred eveent to a funcctional requ uirement. Thhe concept of o FRI was used to quaantify the riisk of fflooding byy Zonensein et al. (20088), where th he componeents (p) andd (c) are reprre-

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sennted by the interaction of a numbeer of factorss, which aree weighted towards eaach other expressed by their reelating charracteristics. For sew wer systemss failure moodes identifi fied with CC CTV inspecttion or operrationn and mainntenance do ocumentatioons have to o be analyzzed to quanntify risks for f funnctional reqquirements. Therefore the failure modes described witth the CCT TV insppection schheme DWA – M 143-22 (1999) were classifieed with resspect to und desireed events which w may cause c a reduuction of fu unctional req quirements.. The classiificatiion follows the Austriaan school grrade system m, where 1 iss the best (iin this case no inflluence) andd 5 is the worst w grade (highest in nfluence). For examplee according to the undesired event “colllapse” failuure modes like crackss, open joinnts, displacced joinnts and fracttures with a severe exttent were deeclared to haave a gradee 4 or 5 (Tab ble 2). Tab ble 2: Example failurre modes w with a hig gh probabiliity to causse a collap pse (bassed on ATV – M 143-2, 19 999)

Furtherr for already y inspected sewers, thee main influencing paraameters on the t m of innterest can be b identified using a loogistic regreesocccurrence of the failure modes sionn analysis. Both intrinssic factors aand surroun nding condiitions are inncorporated in the analysis. The T statisticcal analysis has the pu urpose to prrovide degraadation bassed hazzard probabiilities for seewers whichh were not inspected i so o far or werre not inspected ffor a longerr time. To anallyze the con nsequences of sewer faailure according to the functional rer om floodingg” the most vulnerable sewers of a system, due d quirement “prootection fro to ssewer collaapse (full crross sectionn reduction)) were iden ntified by m means of Vu ulNettUD (Mödeerl et al., 20 009). Vulneerabilities an nd hazard probability p aare then com mbinned in a spattial overlay following tthe FRI con ncept. The seewers are raanked accorrding to their FR RI which in n this case pprovides thee risk of red ducing the functional rer quirement “prrotection fro om floodinng” due to the undesirred event ““cross sectiion redu duction” cauused by sewer collapse.. Furtherr it is taken into accounnt that undeesired eventts have effeects on several funnctional requuirements. For examplle a “collap pse” affectss not only tthe function nal requ quirement “pprotection from fr floodinng” but also o the requireements “enddangerment of adjaacent infrasstructure” and a “mainttaining the flow (dry weather)”. To keep the t num mber of calcculated FRIIs and the nnumber of hazards h takeen into accoount manageeable,, the selecttion of undeesired evennts follows a performaance investiigation of the t

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entire system. Therefore PIs are calculated and compared with service levels due to national standards and utilities targets (Kretschmer et al., 2011). For functional requirements responsible for the major performance deficits the FRIs are calculated. Bivariate Logistic Regression Analysis (LRA) of sewer failure modes As described in Ariaratnam et al. (2001), Davies et al. (2001) and Ana et al. (2009) the regression models have a strong potential to describe the relationship between a response variable (sewer degradation/failure mode) and one or more explanatory variables (intrinsic and surrounding conditions). The logistic regression model describes the relationship between a binary outcome variable (y), where in the case of our analysis: 0 is representing “no hazardous failure mode” and 1 is representing “hazardous failure mode” and a set of k predictor variables (x1,…,xk). The mathematics behind the regression is briefly described as follows. It was amended from the description in Ana et al. (2009) and Dayton (1992) which give a more detailed overview about logistic regression analyses. The main characteristic of the logistic regression is that the outcome variable (y) is not modelled directly but the probability associated with the values of y is provided. Given that y has values of either 1 or 0, the hypothetical population proportion of cases for which y = 1 is defined as π = P (y=1) and the proportion of cases for which y = 0 is 1- π = P (y = 0) and the odds of having y=1 are equal to π/(1-π). The LRA is based on a linear model for the natural logarithm of the odds (i.e., the log-odds) in favour of y = 1 known as the logit function: k ⎡ P ( y = 1) x1,...xk ⎤ ⎡ π ⎤ log ⎢ = log = α + β x + ... + β x = α + ⎢ ⎥ ∑β jxj 1 1 k k ⎣1 − π ⎥⎦ j =1 ⎣⎢1 − P ( y = 1) x1,...xk ⎦⎥

(1)

Where α is the intercept parameter (or constant) and βj representing the logistic regression coefficients (or parameters) for the predictor variables (or covariates) k. Using an exponential transformation equation (1) can be converted to the probability that y =1: k

α + ∑ β jxj

π = P( y = 1) x1,...xk =

j =1

e

k

α + ∑ β jxj

1+ e

j =1

One main purpose of a logistic regression analysis is to compare the odds of an event occurring in one set a (waste type = stormwater) to the odds of it occurring in another set b (waste type = combined). This can be done by calculating the odds ratio (Y):

ψ=

π /(1 − π ) a π /(1 − π )b

When the odds of an event in each of the two sets a and b are equal then Y=1. If Y < 1 then the odds of an event in set a are smaller than in set b, while an odds ratio of Y>1 indicates that the odds of an event in set a are bigger than in set b. The odds

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ratio calculation can be used to define sets of sewers more likely to have specific failure modes than others. To define a model that explains the behaviour of a dataset according to the occurrence of certain events significant covariates have to be derived. There are several statistical methods to form the model. One is the stepwise forwards method which is used in the example described below. The significance of a covariate can be explained with statistical tests like the WALD test. 2

⎛ βj ⎞ ⎟ WALD j = ⎜ ⎜ S .E . j ⎟ ⎝ ⎠ Where βj is the parameter of the jth covariate and S.E.j is the standard error of the maximum likelihood estimation of the parameter. The bigger the WALD value the more significant is the covariate. To compare the quality of two models the qui-squared value or the -2loglikelihood value can be used. The higher the quisquared value the better the model can explain the data behaviour. The LRA with a sample data set of CCTV and sewer data is described in detail in chapter “Case study and results”.

Vulnerability analysis of sewers according to the functional requirement “protection from flooding” due to sewer collapse (VulNetUD) Several types of hazardous events have an impact on urban drainage systems. Anthropogenic hazards (e.g. operational failures, sabotage, land-use change, traffic load), natural hazards (e.g. debris flows, fluvial flooding, landslides, root intrusions), but also sewer deterioration can potentially cause system failures. Such failures can be e.g. reduced sewer cross-section, contaminated water infiltration and blocked combined sewer overflows, among others. VulNetUD emulates the impact of such hazardous events on the infrastructure by a sensitivity analysis (Campolongo et al., 2007) of component parameters. For that purpose, parameter variation according to the impact of a hazardous event can be applied by several settings. For instance, blockage of CSO facilities is simulated by setting sequentially the geometry of each weir opening to zero. VulNetUD evaluates the sensitivity of each modified component of the infrastructure system by calculation of several hydraulic and water quality performance indicators for the entire system (see steps 1 and 2). The sensitivity is regarded as vulnerability as the parameter variation emulates a specific hazard impact. In a next step, a spatially referenced vulnerability map is created by joining the calculated indicator values to the location of the corresponding component (Figure 1 steps 3 and 4). Outputs of the software are different vulnerability maps depending on the chosen settings. For instance, the sewer collapse vulnerability map indicates locations where a total loss of sewer capacity is most harmful.

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Figure 1: Vulnerability analysis with the “Achilles Module” and VulNetUD

CASE STUDY AND RESULTS Sample network: The sample system has a length of 85km and comprises 2833 conduits. It was recently (2008 - 2010) inspected in a CCTV campaign. A first structural condition classification lead to the result that 7 km (8%) sewers are in severe degradation state. This sample is part of a sewer system with a total length of 850 km where a yearly renovation rate of 1% can be realized. The utility currently runs a selective inspection campaign based on 3 parameters (Diameter, Vintage and Material). The system is structured into 28 inspection zones. The inspection campaign started at the zones with the highest amount of sewers expected to be in a bad condition state due to their age and material. Due to the first results a more detailed maintenance prioritization method is of interest to better focus on inspection as well as on renovation needs. After these first campaigns of inspection the described risk oriented sewer classification method based on CCTV data is expected to provide a more detailed decision support for selective sewer inspection. Applying the classification shown in Table 2, 252 (5,3km) conduits of the 2833 (85km) conduits had failure modes classified to have a high probability to cause a collapse. These 252 conduits are declared to have the value y=1 of the dichotomous outcome variable (event/no event) in the bivariate regression analysis. For the sample system further an existing hydraulic model (SWMM) was adapted for the use in VulNetUD. To analyze the FRI for the functional requirement “endangerment of adjacent infrastructure” GIS Data of the water supply and tramway system as well as high traffic roads were included in the analysis. LRA of the collapse probability To define the significant covariates for the event “sewer collapse” a stepwise forward regression method was applied. In Table 3 the covariates and variables which were incorporated in the analysis are listed. A bivariate correlation anal-

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ysis was used as a prior step. According to this analysis the forward regression was started with the covariates correlating the most with the event y=1. Table 3: Covariates and variables considered in LRA Covariates Length

Variable Type Variables(Shortcut) metric

Variables(Names)

Data Source utility GIS Database

Frequency

SewageType

nominal

SS SWS CS

Sanitary Sewer Stormwater Sewer Combined Sewer

utility GIS Database

1818

AC

Asbestos Cement utility GIS Database Brickstone Cast Iron Concrete Ductile Iron glass fiber reinforced plasic Plastic Stoneware

Material

nominal

BS CI Con DI GRP P STW

Profile

nominal

A B C

Width Depth Vintage

metric metric metric

Soil Type

nominal

traffic load

metric

792 222

Ovoid Mouth Circular

96 28 14 1006 516 58 103 1011 175

utility GIS Database

400 2257

CCTV

C L S U

Clay Lime Sand unknown

communal soil map

1240 98 1300 194

municipal traffic data

The LRA showed that the main influencing factors on the most hazardous failure modes due to sewer collapse are pipe material, vintage, diameter (width), profile type, sewage type and depth of cover. Sewage type and depth of cover were the least significant (significance values slightly above > 0.05) (Table 4). Nevertheless they were incorporated into the model as the chi-squared values of the full model were higher than without these two covariates. Table 4: Significant covariates included in the regression model b

s

Wald

SewageType

df

5,444

Y

Sig. 2

,066

SewageType(1)

CS

1,068

,474

5,080

1

,024

2,910

SewageType(2)

SS

,831

,504

2,724

1

,099

2,297

Ref.Cat Sewage Type

STW 58,460

7

,000

1,000

Material Material(1)

AC

,102

,540

,036

1

,850

Material(2)

BS

-,730

,800

,832

1

,362

1,107 ,482

Material(3)

CI

-18,371

10584,489

,000

1

,999

,000

Material(4)

Con

Material(5)

DI

Material(6)

1,214

,213

32,577

1

,000

3,367

-2,129

,605

12,380

1

,000

,119

GRP

-,889

1,033

,740

1

,390

,411

Material(7)

P

-,050

,829

,004

1

,952

Ref.Cat Material

STW

,951 1,000

Vintage

-,017

,003

28,225

1

,000

,983

Depth

-,135

,080

2,874

1

,090

,874

Width

-,007

,002

19,236

1

,000

,993

Length

,020

,005

19,095

1

,000

1,020

6,271

2

,043

Profile(1)

A

-,952

,577

2,723

1

,099

,386

Profile(2)

B

-,009

,510

,000

1

,985

,991

RefCat Profile

C 31,099

6,429

23,400

1

,000

Profile

Constant

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Soil type and traffic load, with significance levels beyond 0.2, turned out to be nonsignificant. Therefore in addition to the former covariates (diameter, material and age) 3 further covariates (profile type, sewage type and depth of cover) will be taken into account for a more focused inspection strategy. By analyzing the odds ratios (y) of the variables of different covariates sewer types of the investigated system which are more likely to fail can be identified. For example combined sewers and sanitary sewers have much higher odds than storm water sewers. The odd of combined sewers is almost 3 times higher than of storm water sewers. Compared to the reference material stoneware, except from concrete pipes, which have the highest odd ratios, all other materials have lower or similar odds than stoneware. According to vintage the odds increase only slightly with the age of the pipe. Surprisingly the depth of coverage is only of minor influence. This might be due to the fact that in the investigated area only a small part is affected by heavy traffic loads. To calculate the occurrence probabilities of a failure mode declared to be hazardous to collapse the final regression model was formulated as follows:

π=

ez

1 + ez where z = 31,099 + 1,068 * x CS + 0,831 * x SS + 0.102 * x AC − 0,730 * x BS − 18,371 * x CI + 1, 214 x con − 2,129 * x DI − 0,889 * x GRP − 0,5 * x P − 0,17 * x vin − 1,35 * x d − 0,007 * x w + 0,02 * x l − 0,952 x ov − 0,009 * x mou

For similar parts of the sewer system this model allows to calculate failure probabilities for non inspected sewers. These modeled failure probabilities can be used to calculate FRIs according to collapse in this non-inspected sections. FRI functional requirement “flooding” (FRI-F) To calculate a FRI for a certain functional requirement the probability of an event and the consequences of this event have to be calculated. As described above, the failure modes of the CCTV inspection were classified with respect to specific hazards (e.g. collapse) according to the classification system shown in Table 2 shows both hazardous failure modes of the sample system and a vulnerability map for flooding calculated with VulNetUD for a full cross section reduction and a design storm with a return period of 5 yr. 4 km sewers were identified to be vulnerable to flooding. The conduits of these sewers are located downstream and represent the main sewer of the system part. Although, in total 9 combined sewer overflows are included to reduce the load, relatively high increase of flood volume occurs under collapse condition of main sewer conduits. 5.3 km sewers, this are 6% of the sample system were identified to have a high probability to collapse.

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ure 2: Hazarrdous failure modes (CCT TV - Classificcation) and vulnerability m map “floodin ng” Figu duee to collapse

The callculations of o the FRI-F F have show wn that 0.5 km of the m most vulnerrable sewers on flooding haave a high pprobability to t collapse as well (Figgure 2; Figu ure 3). These seweers should be b investigaated more thoroughly. t Further thee LRA mod del A tto the odds of wass applied too all 2,833 conduits off the sample system. According speecific sewerr characteriistics 3 km m have a hiigh likeliho ood of hazaardous failu ure modes. These are the circcular profilees of the old dest combin ned and sani nitary concreete sew wers. Compaared to the CCTV C insppected sewerrs 50% of th hese sewerss already haave doccumented seevere failuree modes.

Figu ure 3: FRI -F F “flooding” due d to collapsse

FR RI functionaal requirem ment “Not endangerin ng adjacen nt structurees and utiliity services” (FR RI – I) Figure 4 shows thee parameterr incorporatted for the consequence c es analyses of “enndangermennt of adjacent infrastrructure” du ue to seweer collapsee. They weere weiighted againnst each oth her at equall levels. Thee weights can be basedd on cost daata for infrastructuure reconstrruction if avvailable.

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Figu ure 4: Probab bility and con nsequences p parameters fo or calculating g an FRI for ““endangermeent of aadjacent infraastructure” due d to collapsee

The ressults accord ding to FRI--I have show wn that 0.5 km of the ssample systeem havve a FRI-I >= > 4 regard ding the enddangerment of the adjaacent infrasttructure tram mwayy, high traff ffic road and d water suppply network k. If one specific infrasstructure is of inteerest, e.g. thhe water sup pply networrk, this leng gth increasees up to 4.99 km. Hencee a cleaar definitionn of consequences facttors and their weight is important. Therefore, as alreeady mentiooned, cost related quaantificationss of damag ge reconstruuction can be takeen into accoount. Ideentifying seewers with interacting i g FRIs (FRII-F x FRI-II) All FR RIs have to be taken innto accountt for furtheer planning separately as botth sewers with w a high FRI-I and a high FRI--F are a risk for functitional requirremennt reductionns. Nevertheeless a com mbined view of FRIs waas of interesst in this stu udy to dderive interacting affeccts. By com mbining FRII-F and FRII-I in the saample system, only one sewer section waas identifiedd to be hazaardous for both b perform mance targeets. Butt combiningg the FRI-II for endanngering onee adjacent infrastructurre (e.g. water suppply pipes) with w the FR RI for floodinng 0.5 km were w identiffied.

SU UMMARY Y AND CO ONCLUS SION Extendding probabiility driven methodolog gies (Davies et al. 200 1, Ariaratnaam et aal. 2001, Anna et al. 2009, Fuchs-H Hanusch et al. 2011) for fo operationn and maintenannce planningg to a risk oriented o meethodology allows a iden ntifying sew wers, which do nott only have a high pro obability to fail but alsso are respo onsible for a decrease of funnctional requuirements. Especially for the plan nning of deetailed CCT TV campaig gns as a prior stepp for sewer renovationn this metho od helps to prioritize tthe most risskreleevant sewerrs. In the sam mple sewerr system afteer the first CCTV C camppaign 7 km of sew wers were iddentified to o be in a baad condition n state. App plying a rissk assessmeent metthod to derrive the mo ost relevantt sewers reg garding thee reduction of function nal requ quirements (EN ( 752) a length off 1km was identified. Especiallyy the risks of floooding and endangerme e ent of adjaccent infrastrructures duee to sewer ccollapse weere anaalyzed so faar for a sam mple system.. These werre the most important functional rer

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quirements due to the utility targets. Applying a LRA to already inspected sewers the main influencing parameters on hazardous failure modes could be identified. A first verification of the regression model has shown that 50% of the relevant failure modes could be identified with the model. Further the LRA has shown that the currently used covariates (diameter, material and age) for selective sewer inspection panning of the sample system should be extended up to 7 covariates, which identified to be significant on the occurrence of severe failure modes. In a next step of the ongoing research project the LRA is applied to non inspected sewers in the sample system. This allows further validation of the LRA results. Further incorporating new CCTV data into the analysis an additional improvement of the LRA results is expected.

ACKNOWLEDGEMENTS The work is carried out within the project INFOSAN (Strategic Data Acquisition for Sewer Rehabilitation Planning in Austria), co-financed by the Austrian Federal Ministry of Agriculture, Forestry, Environment and Water Management and the participating sewer utilities. The authors are grateful for this support.

REFERENCES Ana, E., Bauwens, W., Pessemier, M., Thoeye, S. Smoders, S., Boonen I. and De Gueldre G. (2009): “An investigation of factors influencing sewer structural degradation”, Urban Water Journal, 6:4, 303-312 Ariaratnam, S.T., El-Assaly, A. and Yang, Y. (2001). “Assessment of Infrastructure Inspection Needs Using Logistic Regression Models.”, Journal of Infrastructure Systems, 12; 160-165 Campolongo, F., Cariboni, J. and Saltelli, A. (2007). "An effective screening design for sensitivity analysis of large models." Environmental Modelling & Software, 22(10), 1509-1518. Dayton, C.M. (1992). “Logistic Regression Analysis”. Stat 474-574, http://bus.utk.edu/stat/datamining/articles.htm, visited 11 June 2011 Davies, J.P., Clarke, B.A., Whiter, J.T., Cunningham, R.J. and Leidi, A. (2001). “The structural condition of rigid sewer pipes: a statistical investigation.”; Urban Water; 3; 277-286 Fuchs-Hanusch, D., Friedl, F. and Kogseder, B. (2011). “Effect of Seasonal Climatic Variance on Water Main Failures in Moderate Climate Regions.”; Conference Proceedings of LESAM 11, Leading Edge in Strategic Asset Management 27.-29. Sep. 2011, Germany Kainz, H., Gangl, G. and Ertl, T. (2006): “Funktionsfähigkeit von Kanalisationsanlagen in Österreich (Reliability of Sewer Systems in Austria)“. Schriftenreihe zur Wasserwirtschaft, Technische Universität Graz: 47, S. A1-A10, ISBN 3-902465-52-2 Kretschmer, F.; Plihal, H.; Fuchs -Hanusch, D.; Möderl, M.; Ertl, T. (2011): „Development of a data filtration method for sewer rehabilitation planning“. Water Asset Management International 7/3, S. 3 - 6.

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Möderl, M., Kleidorfer, M., Sitzenfrei, R. and Rauch, W. (2009). "Identifying weak points of urban drainage systems by means of VulNetUD.", Water Science and Technology, 60(10), 2507-2513. Zonensein J., Miguez M.G., de Magalhães L.P.C., Valentin M.G. and Mascarenhas F.C.B. (2008). „Flood Risk Index as an Urban Management Tool“., 11th International Conference on Urban Drainage, Edinburgh, Scotland, UK ATV-M 143-2 (1999) „Sanierung von Entwässerungssystemen außerhalb von Gebäuden; Teil 2: Optische Inspektion EN 752 (2008). “Drain and Sewer Systems outside buildings.”, European Committee for Standardization

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