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

Small utilities often lack the required amount of data to train machine learning-based models to predict pipe failures, and hence are unable to harness the possibilities and predictive power of machine learning. This study evaluates the generalizability and transferability of a machine learning model to see if small utilities can benefit from the data and models of other utilities. Using nine Norwegian utilities’ datasets, we trained nine global models (by merging multiple datasets) and nine local models (by utilizing each utility's dataset) using random survival forest. Several pre-processing techniques including addressing left-truncated break data and break data scarcity are also presented. The global models and three of the local models were tested to predict the pipe failure of the utilities which were not included in their training datasets. The results indicate that the global models can predict other utilities with sufficient accuracy while local models have some limitations. However, if a representative utility with a sufficiently large (and information rich) dataset is selected, its model can predict the other utility's pipe breaks as accurate as the global models. Furthermore, survival curves for defined cohorts as proxies for uncertainty, and variable importance show that pipes with and without previous breaks behave extremely different. With the understanding of models’ generalizability and transferability, small utilities can benefit from the data and models of other utilities.

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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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