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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Pavement condition monitoring is an important aspect of an efficient Pavement Management System (PMS). However, the monitoring of road sections is a tedious work. Therefore, research communities have come up with deep learning based automatic methods for detecting and classifying road damages. But for a highway authority overlooking a large network of roads, an aggregate measure of road condition is more useful. Therefore, in this work, the performance of different methods for quantifying road condition has been tested with respect to subjective rating at network level. The road condition was quantified using Damage Count (DC), Damaged Area (DA) and Weighted Damage Count (WDC) approaches using algorithms based on YOLOv8 and DeepLabV3 models for detection and segmentation, respectively. An experiment was followed by expert ratings to determine Mean Panel Rating (MPR) for 36 sections on a selected road stretch in Norway. Proposed models to determine Section Condition Rating (SCR) based on the damage count and weighted damage count approaches were able to predict MPR with an R² value of 0.93. Further, the SCR_WDC was found to have a reasonable correlation with International Roughness Index (R² equal to 0.79).