Data-driven metamodels reproduce the input-output mapping of physics-based models while significantly reducing simulation times. Such techniques are widely used in the design, control, and optimization of water distribution systems. Recent research highlights the potential of metamodels based on Graph Neural Networks as they efficiently leverage graph-structured characteristics of water distribution systems. Furthermore, these metamodels possess inductive biases that facilitate generalization to unseen topologies. Transferable metamodels are particularly advantageous for problems that require an efficient evaluation of many alternative layouts or when training data is scarce. However, the transferability of metamodels based on GNNs remains limited, due to the lack of representation of physical processes that occur on edge level, i.e. pipes. To address this limitation, our work introduces Edge-Based Graph Neural Networks, which extend the set of inductive biases and represent link-level processes in more detail than traditional Graph Neural Networks. Such an architecture is theoretically related to the constraints of mass conservation at the junctions. To verify our approach, we test the suitability of the edge-based network to estimate pipe flowrates and nodal pressures emulating steady-state EPANET simulations. We first compare the effectiveness of the metamodels on several benchmark water distribution systems against Graph Neural Networks. Then, we explore transferability by evaluating the performance on unseen systems. For each configuration, we calculate model performance metrics, such as coefficient of determination and speed-up with respect to the original numerical model. Our results show that the proposed method captures the pipe-level physical processes more accurately than node-based models. When tested on unseen water networks with a similar distribution of demands, our model retains a good generalization performance with a coefficient of determination of up to 0.98 for flowrates and up to 0.95 for predicted heads. Further developments could include simultaneous derivation of pressures and flowrates.

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Co-located infrastructure networks such as road, water, and sewer in theory offer the possibility for integrated multi-infrastructure interventions. However, how closely these networks are aligned in space and time determines the practical extent to which such coordinated interventions can be realized. This study quantifies the spatial alignment of the aforementioned infrastructure networks and demonstrates its application for integrated interventions and potential cost savings. It proposes two metrics, namely 1) shared surface area and, 2) shared trench volume, to quantify the spatial relationship (i.e., degree of co-location) of infrastructures. Furthermore, the study demonstrates how the degree of co-location can be used as a proxy for cost-saving potential of integrated interventions compared to silo-based, single-infrastructure, interventions. Through six case studies conducted in Norwegian municipalities, the research reveals that implementing integrated interventions across road, water, and sewer networks can result in potential average cost savings of 24% in urban areas and 11% in rural areas. Utility-specific savings under different cost-sharing scenarios were also analysed. To identify the yearly potential of integrated multi-infrastructure interventions, future work should add the temporal alignment of rehabilitation of infrastructures (i.e., time of intervention need for the infrastructures).@en

Metamodels accurately reproduce the output of physics-based hydraulic models with a significant reduction in simulation times. They are widely employed in water distribution system (WDS) analysis since they enable computationally expensive applications in the design, control, and optimisation of water networks. Recent machine-learning-based metamodels grant improved fidelity and speed; however, they are only applicable to the water network they were trained on. To address this issue, we investigate graph neural networks (GNNs) as metamodels for WDSs. GNNs leverage the networked structure of WDS by learning shared coefficients and thus offering the potential of transferability. This work evaluates the suitability of GNNs as metamodels for estimating nodal pressures in steady-state EPANET simulations. We first compare the effectiveness of GNN metamodels against multi-layer perceptrons (MLPs) on several benchmark WDSs. Then, we explore the transferability of GNNs by training them concurrently on multiple WDSs. For each configuration, we calculate model accuracy and speedups with respect to the original numerical model. GNNs perform similarly to MLPs in terms of accuracy and take longer to execute but may still provide substantial speedup. Our preliminary results indicate that GNNs can learn shared representations across networks, although assessing the feasibility of truly general metamodels requires further work.

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The prosperity of urban life is dependent on its infrastructure. Urban underground infrastructure components (assets) are aging and need regular monitoring, maintenance and rehabilitation. These assets are often placed under pavements and in close vicinity to each other. Managing them in a coordinated way is rational considering costs and disruption of services and communities caused by each intervention on the different assets. Recently, interest in practice as well as in research has grown to manage urban infrastructures in a coordinated way. This article reviews journal articles and grey literature to evaluate managing these infrastructures in an integrated way (i.e. the highest level of coordination) and describe possible obstacles for doing so. This article identifies seven main challenges of integrated multi-infrastructure asset management (IMAM) that need to be addressed by practitioners and researchers. These challenges are related to: (i) dependencies and interdependencies, (ii) data quality, availability and interoperability, (iii) uncertainties in modelling and decision-making, (iv) comparability, (v) problems of scale, (vi) problems of fit and (vii) problems of interplay. This article provides details on these challenges and discusses future research and practical directions.

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Urban drainage systems have developed way beyond the traditional piped combined or separate sewer systems. Many ‘new’ systems are being introduced, ranging from stormwater infiltration facilities to green roofs. However, the widely advocated blue-green infrastructures are typically overlooked by asset managers, which will very likely have detrimental effects on their performance, service life, and wider adoption. In this paper, the working group on Urban Drainage Asset Management (UDAM – https://udam.home.blog/) of the IWA and IAHR Joint Committee on Urban Drainage discusses whether the state-of-the-art knowledge based on conventional sewer asset management is sufficient to develop asset management for blue-green infrastructures (BGIs). The discussion is structured around the five preconditions for effective control and asset management. Results show that asset management for BGIs is still underdeveloped due to a lack of monitoring techniques covering the broad range of BGI benefits and performance indicators, inspection techniques covering relevant failure mechanisms and models describing these mechanisms, maintenance and rehabilitation options, and sufficient support tools to aid inhabitants in the operation and maintenance of their individual BGIs such as green roofs or vegetated swales.

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Sewer asset management gained momentum and importance in recent years due to economic considerations, since infrastructure maintenance and rehabilitation directly represent major investments. Because physical urban water infrastructure has life expectancies of up to 100 years or more, contemporary urban drainage systems are strongly influenced by historical decisions and implementations. The current decisions taken in sewer asset management will, therefore, have a long-lasting impact on the functionality and quality of future services provided by these networks. These decisions can be supported by different approaches ranging from various inspection techniques, deterioration models to assess the probability of failure or the technical service life, to sophisticated decision support systems crossing boundaries to other urban infrastructure. This paper presents the state of the art in sewer asset management in its manifold facets spanning a wide field of research and highlights existing research gaps while giving an outlook on future developments and research areas.@en