Blockages have been reported to account for a significant proportion of reported failures in sewer networks. The malfunctioning of the sewer network from blockages and the subsequent disruption to other public services and flooding may constitute a risk to the environment and human health. Due to the complex nature of underground sewer networks, a reactive approach to blockage maintenance is typically employed. However, although proactive maintenance strategies have been developed, both approaches could be expensive and highlight the need to address the problem with analytics-based methods. Although blockage triggering mechanisms may be known, sewer blockages often appear at random. Thus, it is necessary to improve the understanding of the influential mechanisms involved in forming blockages in sewer networks to support its maintenance and guarantee adequate performance levels. The overall aim of this thesis was to contribute with new knowledge, approaches and methods that can support improved proactive maintenance planning of blockages in sewer networks.

Various methods to achieve the aim have been investigated in relation to asset management planning levels. At the strategic level, blockages and associated performance indicators were employed in conjunction with Poisson and partial least squares regression to assess the performance of sewer networks, including gaining additional insights. At the tactical and operational levels, a procedure was developed. The procedure combines network k-function, geographically weighted regression and random forest ensembles. The network k-function analysis explains the significance of the spatial variation of blockages. The Geographically weighted Poisson regression (GWPR) investigates the degree of influence of explanatory factors on increased blockage propensity in differentiated segments of the sewer networks. Thirdly, the random forest ensembles was used to predict pipes with blockage recurrence likelihood. A proposed conceptual framework was applied at all asset management levels to assess the state of data-driven integrated asset management (IAM), based on data quality assessments, interoperability evaluations between IAM tools, and data collection and informational benefits analysis. 

Results from demonstrating the methods with data from the Swedish waters statistical database and three Swedish municipal sewer networks, namely A, B and C, are presented. Blockage related performance indicators showed that the average blockage rate in medium sized networks was 2-3 times the rate in other sewer networks in Sweden. Furthermore, sewer maintenance strategies were suspected to be ineffective, and increased proactive strategies may improve maintenance efficiency. The procedure in networks A, B and C indicated that the clustering of recurrent blockages maybe linked to an increased need for flushing-related maintenance in sewer pipe networks. The degree of influence between investigated factors and increased blockage propensity indicated that these relationships were not global (not the same in all locations) within and between the sewer networks for networks A, B and C. These non-stationary relationships were observed to occur in various forms, i.e. adequate self-cleaning velocity showed positive and negative correlations in different locations. The networks with relatively more substantial spatial clusters of blockages, higher data quality and availability were observed to have a higher mean prediction accuracy. The applied conceptual framework showed that intuitive asset management characterised the current state of blockage management in the municipal sewer network C with medium to good data quality and low interoperability.

Sewer blockages can cause overflows and flooding, with consequences such as damage to property and environmental pollution, risks to public health and economic loss. Despite the causes being understood, blockages in sewer networks may occur unpredictably. The responsible staff for sewer networks at water utilities need to efficiently determine the most effective action (what), the specific network location needing attention (where), the optimal timing for intervention (when), and the appropriate remedial task (how), especially given the unpredictability of blockages. Today a reactive approach to asset management and maintenance is often adopted. Additionally, data availability, quality and interoperability between systems are not always at levels that can support decided objectives, proactive maintenance planning and asset management of pipe networks. Thus, the aim of this thesis is to propose and evaluate approaches that can support analytics-driven maintenance planning and asset management for sewer networks. These approaches aim to contribute to mitigating the impact of siloed data structures and enhance the understanding of blockage root causes from a spatial perspective.

In this thesis, the challenges of data management in the asset management of pipe networks were investigated through focus group workshops and questionnaire surveys. A conceptual framework was developed based on findings from focus group workshops and surveys. The framework combines data quality assessments, interoperability evaluations between asset management tools, data collection, and informational benefits analysis. This framework aimed to identify the presence of data silos and plausible pathways towards more data-driven data management strategies. A performance assessment combining performance indicators associated with blockages and partial least squares regression (PLS) was conducted to draw inferences that could be useful at a strategic level. Furthermore, a spatial heterogeneity assessment of blockages and factors affecting blockages was carried out. This approach combined network kernel density estimation (NKDE), network k-function, and geographically weighted Poisson regression (GWPR). Lastly, a vulnerability assessment was carried out that combined topological analysis using edge-based centrality measures and network cross-k-function. These approaches were applied to three sewer networks.

The focus group workshops and questionnaire surveys identified several challenges affecting data management in the context of pipe network asset management. Many of the challenges could be ascribed to issues related to data quality and interoperability. Results from the preliminary application of the conceptual framework showed how it could be applied for identifying data silos and pathways to data-driven decision-making towards proactive management blockages in sewers. The observed spatial trends and patterns from network k-function analysis and network kernel density estimation showed spatial variability in the occurrence of blockages (single occurring and recurring). Geographically-weighted Poisson regression analysis showed spatial heterogeneity in factors influencing blockage propensity. The network cross-k-function analysis demonstrated that pipes with historical blockage incidents tend to be clustered around critical pipes with higher centrality values. These results could support vulnerability assessments in sewer networks and the development of targeted maintenance strategies. These approaches together could aid data-informed maintenance planning and asset management at the strategic, tactical and operational levels.

Avloppsstopp kan orsaka översvämningar vilket kan medföra konsekvenser som skador på egendom och spridning av föroreningar och leda till ekonomiska förluster samt risker för folkhälsan. Trots att orsakerna till avloppsstopp är kända, inträffar ibland oförutsedda avloppsstopp i ledningsnäten. De som ansvarar för avloppsledningarna behöver kunna fastställa effektiva åtgärder (hur), den specifika punkt i nätverket som behöver åtgärdas (var) och den optimala tidpunkten för åtgärd (när). Idag sker ofta åtgärder på ledningsnät reaktivt. Vidare är inte alltid data för analys tillgänglig eller av god kvalitet. Det finns även problem med interoperabilitet mellan system som kan stödja uppsatta mål för proaktiv underhållsplanering och tillgångsförvalting av ledningsnät. Därför är syftet med denna avhandling att föreslå och utvärdera metoder som kan stödja analytiskt driven underhållsplanering och tillgångsförvalting av avloppsledningsnät. Dessa metoder syftar till att minska negativa effekter av så kallade datasilos och förbättra förståelsen av grundorsaker till avloppsstopp utifrån ett spatialt perspektiv.

I denna avhandling undersöktes utmaningar med datahantering inom tillgångsförvalting i ledningsnät genom fokusgruppsworkshopar och enkätundersökningar. Ett konceptuellt ramverk utvecklades, baserad på resultat från workshoparna och en av enkäterna. Ramverket innefattade användning av datakvalitetsbedömningar och utvärdering av interoperabilitet mellan verktyg för tillgångsförvalting och datainsamling. Vidare analyserades fördelar med att identifiera förekomsten av datasilor och tänkbara tillvägagångssätt för att nå mer datadrivna strategier för datahantering. En prestandautvärdering som kombinerade prestandaindikatorer relaterade till avloppsstopp som kombinerades med regressionsanalys med minsta-kvadrat-metoden genomfördes för att dra slutsatser som kan vara av nytta på strategisk nivå. Vidare gjordes en spatial bedömning av avloppsstoppens förekomst och deras variationer över ledningsnätet samt faktorer som påverkar avloppsstopp. Denna analys kombinerade en täthetsanalys (kernel density estimation) med en network k-function samt en geografiskt viktad Poisson-regression analys. Slutligen genomfördes en sårbarhetsbedömning som kombinerade en topologisk analys baserad på grafteori och network cross-k-function. Metoderna tillämpades på tre avloppsnät.

Fokusgruppsworkshoparna och enkätundersökningar identifierade en mängd utmaningar som påverkar datahantering i tillgångsförvalting av ledningsnät. Flera av utmaningarna kunde tillskrivas problem relaterade till datakvalitet och interoperabilitet. Resultaten från den preliminära tillämpningen av det konceptuella ramverket visade hur det kunde användas för att identifiera datasilor. De observerade spatiala trenderna genom analysen med network k-function och täthetsanalysen visade spatial variabilitet i förekomsten av avloppsstopp. Den geografiskt viktade Poisson-regressionsanalysen visade spatial heterogenitet i faktorer som påverkar förekomsten av avloppsstopp. Analysen med Network cross k-function visade att ledningar med historiska incidenter med avloppsstopp tenderade att grupperas kring rör med högre centralitetsvärden som med det bedömdes som mer kritiska för ledningsnätens funktion. Dessa metoder skulle tillsammans kunna bidra till en mer datainformerad underhållsplanering och tillgångsförvaltning på strategisk, taktisk och operativ nivå.