Aun Dastgir

One way to enhance the resilience of urban stormwater networks is to identify critical components of the network for targeted management and maintenance strategies. However, tackling multiple-pipe failures poses a significant hurdle due to the computational burden of conventional methods, making it impractical to analyze all potential combinations of pipe failures. To address this research gap, this study proposes a novel method based on complex network analysis to determine critical pipe failure combinations. The method incorporates network topology and estimates flooding impacts based on graph measures instead of solving complex hydraulic equations. The method is tested on two case studies with varying loop degrees and for different failure levels and compared with the state-of-the-art hydrodynamic modeling method (HMM) in terms of accuracy and computational time. Results show that the proposed graph theory method (GTM) can be used to identify the most critical pipe failure combinations. However, the accuracy of the GTM decreases slightly with increasing failure level and with increasing loop degree of the network. A hybrid graph-hydrodynamic model (GHM) is also developed as part of the study which combines advantages from both GTM and HMM.

Modelling multiple pipe failure scenarios in stormwater networks is a challenging task due to the computational burden of conventional methods. In this context, this study proposes a hybrid graph hydrodynamic model (GHM) that combines the advantages of graph theory and hydrodynamic modelling to enhance the identification of critical pipe failures (i.e., computationally efficient and high accuracy). First, based on graph theory, critical pipe combinations are identified, followed by hydrodynamic modelling to accurately assess the flooding impacts of these critical combinations. The findings underscore the effectiveness of GHM, particularly in scenarios requiring numerous simulation runs.

Nature-Based Solutions (NBSs) are decentralised and planted system elements with multiple benefits, requiring sufficient irrigation during dry weather periods to ensure plant health. In this work, the effects of the large-scale implementation of NBSs in the city centre of Klagenfurt from a water supply perspective are investigated, combining hydraulic analysis with water resource availability. As the large-scale implementation of NBSs in public squares shows, a coordinated NBS implementation strategy is required to ensure compatibility with the city’s water resources and infrastructure. This also emphasises the importance of alternative water sources for sustainable operations.

Water distribution networks (WDNs) are critical infrastructures prone to vulnerabilities which lead to failures. Identifying vulnerable components, especially multiple pipe failure combinations, is crucial for effective management and ensuring high reliability. Hydraulic simulations are commonly used for analysing the criticality of WDN, but are time-consuming and highly data-reliant, limiting the number of testable combinations. To address these limitations and constraints, a graph-based method is proposed to quantify the impact magnitude of multiple pipe failure scenarios on performance, enabling the identification of critical combinations. The proposed graph-based approach utilizes structural and topological characteristics of WDNs as well as spatial demand distribution to replicate hydraulic behaviour. The accuracy of the approach is assessed by testing it on three case studies with various pipe failure combinations, and the results are compared with hydraulic analyses. The results demonstrate a strong correlation (Spearman coefficient > 0.75) between graph-based ranking and state-of-the-art hydraulic-based ranking. Additionally, the method exhibits a significant computational gain factor of greater than 30 compared with the hydraulic-based method, rendering it valuable for actively exploring a wide range of critical pipe failure combinations and devising countermeasures. Furthermore, a hybrid-based method that integrates both the graph and hydraulic-based methods is proposed for enhanced accuracy and robust assessments.

Critical infrastructures such as water, power, telecommunications, and transportation systems are an important part of human life in an urban environment, which are vulnerable to disasters, failures, asset forfeiture, and sabotage. The focus of this work is on water distribution networks (WDNs) including different hydraulic elements such as pipes, tanks, valves, and reservoirs to transport drinking water from central treatment plants or sources to consumers. Thereby, efficient identification of system parts in a WDN that are vulnerable to failures is important for efficient management and to provide high reliability. Therefore, often hydraulic simulations are applied, which are computationally intensive and impractical for large networks and multiple pipe failures.In this study, a graph theory-based analysis is used to identify the critical elements (pipes) in a WDN based on "demand edge betweenness centrality (EBCQ)". Therefore, the connectivity of the network based on spatial layout is modelled using a mathematical graph-based approach that represents the topology of the WDN and incorporates hydraulic factors to imitate hydraulic behavior. The mathematical graph consists of #N (vertices) connected by a set of #E (edges) corresponding to nodes and pipes respectively. The ratio of length and diameter is used as edge weight to determine the shortest path, and adds the demands of the nodes to obtain EBCQ values for all edges in that path. In case of pipe failures, the corresponding edge is removed, and the EBCQ of the new graph is calculated and compared with the maximum possible flow to find overloaded and affected edges. Thereby, single and multiple pipe failures are investigated with this method.The method is applied to a benchmark case study and to a real network of an Alpine municipality in Austria. Furthermore, the results of the graph-based method are compared with the results from hydraulic modelling in terms of accuracy and computational time. The first results show only slight differences in the results of the graph-based method compared to those from hydraulic modeling. Further, the graph-based method is able to identify the same order of critical pipes with low deviation compared to those from the hydraulic model. Additionally, the computational time and data requirements, in the calculation of pipe criticality by graph-based approach is significantly less compared to the hydraulic modelling method. This method is useful in disaster or contamination scenarios where many scenarios or combinations are required.Funding: The project “RESIST” is funded by the Austrian security research programme KIRAS of the Federal Ministry of Finance (BMF).

Enhancing resilience of drainage networks is a crucial practice to protect both humans and nature. One way to enhance resilience is to identify critical parts of drainage networks for targeted management and maintenance strategies. While hydrodynamic modelling approaches for identification are computationally intensive, in this study, a novel method based on complex network analysis is used to determine the most critical pipes in a benchmark and a real network of an Alpine municipality. For evaluation, the results of the proposed graph method are compared with hydrodynamic simulations in terms of accuracy and computational time. Results show that the proposed method is very accurate (R2 = 0.98) for branched benchmark network while the accuracy reduces slightly for the more complex real network (R2 = 0.96). Furthermore, the accuracy of the proposed method decreases with increasing loop degree and when the system is pressured with higher return period rainfall. Although the outcomes of the proposed method show slight differences to hydrodynamic modelling, it is still very useful because the computational time and data required are much less than a hydrodynamic model.

Infrastructure benchmarking for semi-real urban stormwater networks